Dr Arnaud Boucher

Prof Anis

Prof Ali Douik

Prof Nawres Khlifa

Prof Prof Mireille Garreau

Samia

Le Creusot

Khawla Brahim

INTRODUCTION

T his chapter explains the clinical motivation and thesis objectives for automatically segmenting the myocardial muscle, including Acute Myocardial Infarction (AMI), to help diagnosis and therapy planning. Also, it introduces the thesis outline and the significant publications ensuring from the thesis as a clue of its scientific quality.

1.1/ CLINICAL BACKGROUND Cardiovascular Diseases (CVDs) are the leading causes of mortality worldwide, with an estimated 17.9 million deaths in 2019, mainly due to myocardial infarction (MI), commonly known as heart attack and stroke 1 . Radiologically diagnosing MI in its early phases plays a crucial role in treating and improving clinical outcomes. In recent decades, significant research works have been developed to improve the prognosis of cardiac diseases and therefore reduce CVDs deaths.

MI is a medical condition preventing blood supply to the cardiac muscle, caused by the blocking of coronary arteries (see Figure 1.1). The size of infarcted myocardial tissue is impacted by the time taken until treatment is performed. Acute complications may progress towards Heart Failure (HF) if the affected heart cannot pump blood sufficiently to the circulatory system's lungs and body [START_REF] Somers | Sleep apnea and cardiovascular disease: An american heart association/american college of cardiology foundation scientific statement from the american heart association council for high blood pressure research professional education committee, council on clinical cardiology, stroke council, and council on cardiovascular nursing in collaboration with the national heart, lung, and blood institute national center on sleep disorders research (national institutes of health)[END_REF].

MI occurs as a result of atherosclerosis, in which plaque builds up inside the artery walls. This build-up makes the arteries progressively narrower and slows blood flow, causing angina. Finally, an area of cholesterol plaque can tear inside of a coronary artery. This rupture results in a blood clot forming on the plaque's surface, which can then completely block blood flow through arteries. If the blockage isn't remedied fast, the heart muscle begins to die. The healthy heart area is substituted with the infarct area.

Medical studies have shown that in AMI, the infarct tissue mainly contains heterogeneous Figure 1.1: Illustration of how a blocked coronary artery leads to MI. The artery inhibits blood flow to the muscle, causing damage to the muscle tissue. 2 regions. The chronic setting develops immediately after MI, and the aim of treatment is the fast recovery of blood flow. There is a lack of a viable myocardium (MYO) with the increased extracellular area. In chronic myocardial infarctions, capillaries in myocardial tissue continue to be impeded after the reperfusion, and a gray zone called peri-infarct exhibits decreased contractile activity. The permanent absence of tissue perfusion is known as microvascular obstruction (MVO), also called the no-reflow phenomenon [START_REF] Mikami | Relation between signal intensity on t2-weighted mr images and presence of microvascular obstruction in patients with acute myocardial infarction[END_REF]. Subjects sustaining MVO zones have higher proportions of MI and raised mortality [START_REF] Jaffe | Microvascular obstruction and the no-reflow phenomenon after percutaneous coronary intervention[END_REF].

There is a potent correlation between the peri-infarct region, ventricular arrhythmias, and unexpected death [START_REF] Rubenstein | A comparison of cardiac magnetic resonance imaging peri-infarct border zone quantification strategies for the prediction of ventricular tachyarrhythmia inducibility[END_REF][START_REF] Yan | Characterization of the periinfarct zone by contrast-enhanced cardiac mri is a powerful predictor of postmyocardial infarction mortality[END_REF]. Coronary revascularization for curing AMI is related with MVO in the scar core and peri-infarct area at the border of infarcts.

Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the reference standard for detecting MI [START_REF] Kim | Relationship of mri delayed contrast enhancement to irreversible injury, infarct age, and contractile function[END_REF][START_REF] Simonetti | An improved mr imaging technique for the visualization of myocardial infarction[END_REF]. Myocardial LGE-MRI studies are achieved approximately 10 minutes following injection of the gadolinium-based contrast agent. With this method, healthy and infarct tissues are distinguished by their altered wash-in and washout contrast agent. By the automatic extraction of the geometry of the MYO, cardiologists can conclude the functionality of the damaged muscle, which supplies guidance on further treatment of the patient. As shown in Figure 1.2, the MVO area is defined as the hypointense region within the core of the MI on LGE-MRI sequences achieved at 3 min- The MI occurs hyperintense on the late LGE image, with a subendocardial region (black arrow) corresponding to MVO. On the early LGE sequence, the MYO is still saturated with gadolinium, although the MVO already occurs hypointense (black arrow) [START_REF] Bouleti | The no-reflow phenomenon: state of the art[END_REF].

Today, the accurate segmentation of the MYO is of high relevance in clinical practice.

Clinicians achieve manual contouring of the MYO as an essential first step in analyzing cardiac images. An example of a delineation of a patient's slice can be seen in Figure 1.3. Nevertheless, as the workflow that clinicians face, manual segmentation is usually time-consuming, depends on expert experience, and suffers from expert variability.

Consequently, there is an increasing need for automated segmentation methods. This requirement was primarily justified through the success rate performed by these methods [START_REF] Goodfellow | Deep learning[END_REF].

1.2/ GOALS AND ORIGINAL CONTRIBUTIONS

Cardiac MR (CMR) imaging is of the greatest interest for morphological evaluation and diagnosis of different CVDs. Deep learning (DL) networks have significantly boosted state-of-the-art segmentation performance in Cardiac MRI (CMRI). Nevertheless, previous techniques have mainly applied various pre-processing stages to segment lowresolutions images. This thesis aims to develop a DL model using prior constraints performing automatic segmentation of the MYO of patients affected by diseased myocardial tissue (MI, MVO). Specifically, we proposed three end-to-end models (SegU-Net, SPIU-Net, ICPIU-Net) for myocardial segmentation in CMRI. Research on cardiac segmentation has been extensively promoted through benchmarking datasets, notably those associated with MICCAI challenges. We evaluated these approaches' performance on the EMIDEC dataset to conclude which is well adapted to this task. Therefore, the goal is to build an automated system that produces a delineation of the myocardial regions shown as drawn boundaries over the LGE-MRI, presented in Figure 1.3. Different metrics evaluate the model's performance for finding the best-performing model by comparing the predicted results with the manual delineations. Experimental results highlight the efficiency of the proposed models compared to ground truth and state-of-the-art segmentation algorithms.

Resources on the NVIDIA Tesla V100 with four embedded GPUs were available to fulfill the computational demands.

1.3/ THESIS ORGANIZATION

In response to the HF problem described above, this thesis is composed of seven chapters. In the second one, we give an overview of the clinical context, where we present the anatomy and the functioning of the heart. Chapter 3 describes some fundamental concepts within the field of DL. In Chapter 4, we comprehensively provide a brief review of existing methods dedicated to medical segmentation tasks. We then describe the data material given by the EMIDEC challenge. Chapter 5 details the three general frameworks underlying the prior constraints based on inclusion and classification coupled with regularization penalty terms and the fusion of SegNet and U-Net architectures to segment all myocardial diseases in LGE-MR images. According to several metrics, Chapter 6 focuses on evaluating the performance of proposed algorithms (slice-by-slice 2.5D, a 3Dto-3D, and constrained with inclusion and classification 3D-to-3D). Chapter 7 summarizes the work and outlines some future directions for further improvement in the field. -1st Author.

1.4.2/ CONFERENCE PROCEEDINGS

• Brahim, K., Qayyum, A., Lalande, A., Boucher, A., Sakly, A., and Meriaudeau, F.: A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases. In 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. [START_REF] Young | Apparent changes of appearance of inversion-recovery images[END_REF][2][3][4][5][6]. IEEE. (2020, November) [START_REF] Brahim | A 3d deep learning approach based on shape prior for automatic segmentation of myocardial diseases[END_REF]. -1st Author.

• Brahim, K., Qayyum, A., Lalande, A., Boucher, A., Sakly, A., and Meriaudeau, F.: A deep learning approach for the segmentation of myocardial diseases. In 2020 25th

International Conference on Pattern Recognition (ICPR) (pp. 4544-4551). IEEE.

(2021, January) [START_REF] Brahim | A deep learning approach for the segmentation of myocardial diseases[END_REF]. -1st Author.

1.4.3/ INTERNATIONAL WORKSHOPS

• Brahim, K., Qayyum, A., Lalande, A., Boucher, A., Sakly, A., and Meriaudeau, F.: Efficient 3D deep learning for myocardial diseases segmentation. In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 359-368). Springer, Cham. (2020, October) [START_REF] Brahim | Efficient 3d deep learning for myocardial diseases segmentation[END_REF]. -1st Author.

1.5/ CONCLUSION

Medical imaging is currently a research field in active expansion. Many studies are used nowadays to put in evidence infarction tissues and lesions attributed to other diseases such as hypertrophic cardiomyopathy. This thesis contributes to enhancing the diagnosis and monitoring of MI on LGE data, leading to better prevention and higher survival opportunities for the patient. Over the past few decades, DL is rapidly growing in image analysis, particularly CMRI segmentation. Designing a deep neuronal network is exciting guidance for our research. The primary objective is to develop new DL tools to assess risk, quantify and predict myocardial tissues' presence on a set of contrast-enhanced acquisitions.

2 CLINICAL CONTEXT T he cardiovascular system plays a significant role in the functioning of living organisms. In this chapter, we briefly describe the heart anatomy and its function. Then, we outline the most used imaging modalities for diagnosing and monitoring different cardiac conditions. In particular, MI can be identified at early phases by radiologists from CMRI. However, infarct region segmentations depend on intra-and inter-observer variability, which is critical to determining suitable therapy. Consequently, a computer-aided method should be carried out to detect scars, affording clinicians more consistency.

2.1/ HEALTHY CARDIAC STRUCTURE AND FUNCTION

2.1.1/ CARDIAC STRUCTURE

A detailed review of the cardiac structure is provided in [START_REF] Anderson | Cardiac anatomy revisited[END_REF]. The heart is a muscular organ responsible for pumping oxygen-rich blood throughout the body. It is geometrically located at the chest's center between the right and left lungs.

The heart comprises two sides, split into two parts: two atria (upper chambers) and two ventricles (lower chambers), as illustrated in Figure 2.1. The atria are connected to veins and proceed as reservoirs for venous blood transits, with a pumping function for assisting ventricular filling. The ventricles are connected to arteries and primary pumping chambers that distribute blood to the systemic circulatory (left ventricle (LV)) and pulmonary circulations (right ventricle (RV)). The septum separates the two sides of the heart.

The heart wall consists of three layers: the epicardium, the MYO, whose thickness values vary between 6 and 16 mm, and the endocardium. The epicardium is the outer layer that helps lubricate and preserve the outside of the heart. The MYO is the muscular layer of the heart and comprises cardiac muscle tissue responsible for the blood pumping through the organism. The endocardium is the innermost layer that protects the blood from sticking inside the heart.

2.1.2/ CARDIAC CYCLE

The heart is a muscular pumping organ, reiterating the same cardiac cycle. Its frequency is usually expressed in beats per minute. Each cardiac cycle is split into two primary periods: diastole (relaxation), when the heart fills with blood, and systole (contraction), when the heart pumps the blood. These two periods depict a sequence of all the events that occur with every heartbeat. An electrocardiogram (ECG) detects the electrical impulses in the heart using electrodes attached to the skin. The graph of electrical activity through the heart includes three main waves: P, which represents depolarization of the atria; QRS complex, which represents depolarization of the ventricles; and T, which represents repolarization of the ventricles [START_REF] Leonard | Pathophysiology of heart disease: A collaborative project of medical students and faculty[END_REF]. ECG is a standard test used to diagnose heart rhythms and electrical activity. Modifications in the typical ECG graph appear in several cardiac abnormalities, such as insufficient coronary arteries blood flow and cardiac rhythm troubles. Figure 2.2 shows the relationship between the cardiac cycle and ECG.

A great physician's interest has been dedicated to computing the LV mass and volume at the most significant contraction's time (end-systole) and the utmost filling's time (enddiastole) (see Figure 2.3). 1 Source:https://basicmedicalkey.com/structure-and-function-of-the-cardiovascular-and-lymphatic-systems This sub-phase accounts for most of the ventricular filling with blood.

Ventricular diastole or atrial systole:

The atria contract and then complete the ventricular filling.

2.1.2.2/ SYSTOLE

The Systole period pushes the blood from the ventricles to the pulmonary and systemic circulations. This period is divided into two stages:

1. Isovolumic contraction: As the ventricle starts to contract, the pressure surpasses that of the respective atrium, causing the atrioventricular valves to shut. Again, all the valves are closed, preventing blood from being ejected.

Ventricular ejection:

When ventricular pressure overtakes that of the aorta and pulmonary arteries, the semilunar valves open. The LV and RV eject the blood to the aorta and pulmonary arteries, whereas the atrioventricular valves remain closed. And the cycle keeps repeating until the individual is dead.

2.2/ CARDIAC IMAGING MODALITIES

In terms of follow-up, treatment monitoring, and diagnosis purposes, several non-invasive imaging modalities have been made to provide clinicians a vision of the patient's medical condition. We introduce the three common imaging modalities utilized in cardiology, concentrating on MRI, the primary modality used in this thesis [START_REF] Celebi | Current cardiac imaging techniques for detection of left ventricular mass[END_REF][START_REF] Rehman | Cardiac imaging[END_REF]. Every technique is based on various image acquisition principles.

2.2.1/ MAGNETIC RESONANCE IMAGING

Magnetic resonance imaging (MRI) is a comparatively recent imaging modality developed in the '70s. This technology is based on spin's physical quantum mechanical property. MRI is the favored modality for the guidance of cardiac interventions thanks to its multiple advantages, including high-quality, soft tissue contrast, and non-invasive aspects. Its use in cardiology is of greater focus through the last decades for disease segmentation and treatment planning, and therefore the requirement for fast and accurate segmentation methods for identifying the diseased region is critical. This technique depends on powerful magnetic fields, field gradients, and radio waves to acquire images into the body. Indeed, a uniform magnetic field (most of the utilized clinics' scanners perform at 1.5 or 3 Teslas) aligns the hydrogen atoms's spin. Then, a further magnetic field is superimposed to re-orient the aligned spins. The time that the spins takes to realign with the magnetic field relies on the environment and the molecules' chemical nature, which permits the scanner to reconstruct an image presenting various contrasts on the biological structures.

Through CMR imaging, it is possible to characterize myocardial tissues. T1 and T2 map-

Vertical long axis (two-chamber view (VLA))

The two-chamber view is aligned from the axial plane and passes through the center of the mitral valve and the LV apex. This view provides an overview of the LV and left atrium (LA).

Horizontal long axis (four-chamber view (HLA))

Horizontal long axis aligned orthogonal to the VLA, passing through the center of the mitral valve and LA and continuing through the long axis of the LV. This view displays all four chambers of the heart, the left and right atria, and ventricles (LA, RA, LV, RV).

Short axis (SA)

The short axis view yields excellent cross-sections of the LV and RV. This view is defined such that a series of slices are perpendicular to the two long axes (VLA, HLA) and often is utilized for viability analysis in MI.

2.2.1.2/ CARDIAC CINE IMAGING

An accurate evaluation of cardiac morphology and function is crucial for diagnosing cardiac diseases [START_REF] Frangi | Three-dimensional modeling for functional analysis of cardiac images, a review[END_REF]. Dynamic image sequences (cine) are essential in obtaining complete heart function information throughout the cardiac cycle [START_REF] Pfeiffer | Cardiac mri: a general overview with emphasis on current use and indications[END_REF]. Indeed, cine CMRI is the fundamental method for quantifying the heart's global and regional contractile function [START_REF] Frangi | Three-dimensional modeling for functional analysis of cardiac images, a review[END_REF][START_REF] Sakuma | Fast magnetic resonance imaging of the heart[END_REF][START_REF] Maleki | Practical cardiology[END_REF]. As such, cine imaging is of primary significance to both clinical and research applications of CMRI. In typical cine scans, several 2D slices covering the whole volume of the heart are acquired (see Figure 2 The constitution of a database is usually achieved using a single acquisition recovery technique. Indeed, while being effective at a given task, DL networks tend to depend on the provided data and fail because of differences among images when switching from one acquisition technique to another.

Magnitude Inversion Recovery (MIR)

IR sequences usually use magnitude reconstruction for the translation of the MR signal to pixel intensity. In this approach, pixel intensity depends just on the magnitude of the longitudinal magnetization of a tissue. The performance of such a technique when employing magnitude reconstructed images is very sensitive to the inversion recovery time chosen.

Phase Sensitive Inversion Recovery (PSIR)

Phase-Sensitive IR can eliminate the background phase while retaining the intended magnetization sign during IR [START_REF] Young | Apparent changes of appearance of inversion-recovery images[END_REF]3]. In PSIR, scar tissue has a higher signal than healthy MYO. Contrary to MIR, the PSIR method yields good results on a comparatively extensive range of inversion recovery time. The differences of signal among healthy and infarcted tissues may last longer.

2.2.2/ ECHOCARDIOGRAPHY

Echocardiography is nowadays extensively used for diagnostic tools in cardiology [2]. An echocardiogram (see Figure 2.8) is based on ultrasound in biological tissues. The main advantages of the Echo are its ease of use, low cost, safety (as it does not use ionizing radiation), and rapidity [4]. Due to the variance in the sound reflection, relying on the tissue, the ultrasound image can distinguish inner body structures from internal organs.

During, echo acquisitions the clinician places the ultrasound probe utilizing a gel to promote the waves' transmission across the skin. In addition, ECG electrodes can be applied to gate the acquisition with the cardiac rhythm. Thus, several 2D temporal acquisitions are made in various positions, each taking from 10 to 30 minutes. A more recent method enables real-time three-dimensional echocardiography where 3D acquisitions are made in one heartbeat. This method is of great significance in clinical practice to detect structural abnormalities [START_REF] Goland | A case of arrhythmogenic right ventricular cardiomyopathy[END_REF][START_REF] Poh | Assessing aortic valve area in aortic stenosis by continuity equation: a novel approach using real-time three-dimensional echocardiography[END_REF]. Several studies evaluated the potency of Myocardial Contrast Echocardiography (MCE) for myocardial viability assessment [START_REF] Janardhanan | Usefulness of myocardial contrast echocardiography using low-power continuous imaging early after acute myocardial infarction to predict late functional left ventricular recovery[END_REF][START_REF] Dwivedi | Prognostic value of myocardial viability detected by myocardial contrast echocardiography early after acute myocardial infarction[END_REF][START_REF] Funaro | Incidence, determinants, and prognostic value of reverse left ventricular remodelling after primary percutaneous coronary intervention: results of the acute myocardial infarction contrast imaging (amici) multicenter study[END_REF].

Figure 2.8: 2D and 3D echocardiography of the heart with the 3 most useful views [START_REF] Navarro | Real-time 3d echocardiography in percutaneous balloon mitral valvuloplasty[END_REF].

For radiologists, echocardiography has been recognized as an efficient visualizing and diagnostic tool of LV due to its availability and low cost. However, echocardiography images suffer from specific artifacts (e.g., edge dropout, attenuation, and shadowing), preventing expert interpretation and automated computer analysis [START_REF] Mazaheri | Echocardiography image segmentation: A survey[END_REF]. Therefore, automated LV segmentation of echocardiographic images has been a challenging task. Many advanced methods to echocardiographic segmentation data have been developed [START_REF] Liu | Deep learning in medical ultrasound analysis: a review[END_REF][START_REF] Yang | Finegrained recurrent neural networks for automatic prostate segmentation in ultrasound images[END_REF][START_REF] Milletari | Hough-cnn: deep learning for segmentation of deep brain regions in mri and ultrasound[END_REF]. The CT scan's principle is based on detecting the X-rays intensity, which passe across the anatomy to estimate the "material density" and thus acquire the image of the anatomy.

2.2.3/ COMPUTED TOMOGRAPHY

Electrons are diffused onto a tinny surface, leading to the radiation's emission.

Several X-rays images are fused to generate cross-sectional views of the body. Before acquisitions, the patient can be injected with a contrast medium, a chemical substance that discloses internally in the body's tissues [START_REF] Ropers | Detection of coronary artery stenoses with thin-slice multi-detector row spiral computed tomography and multiplanar reconstruction[END_REF][START_REF] Morin | Radiation dose in computed tomography of the heart[END_REF].

Figure 2.9: Three different views acquired using 3D CT image [START_REF] Roh É | Reduced representation of segmentation and tracking in cardiac images for group-wise longitudinal analysis[END_REF].

3D CT images are significant imaging modalities that provide detailed tissue information to assist diagnosis and planning treatment [START_REF] Doi | Computer-aided diagnosis in medical imaging: historical review, current status and future potential[END_REF]. Nevertheless, anatomical segmentation based on CT images on a broad human body tissue is still challenging due to similarities of image appearance between several structures. Thus, many approaches focused on medical image segmentation from 3D CT images [START_REF] Zhou | Threedimensional ct image segmentation by combining 2d fully convolutional network with 3d majority voting[END_REF][START_REF] Zhou | Deep learning of the sectional appearances of 3d ct images for anatomical structure segmentation based on an fcn voting method[END_REF][START_REF] Trullo | Joint segmentation of multiple thoracic organs in ct images with two collaborative deep architectures[END_REF][START_REF] Gibson | Automatic multi-organ segmentation on abdominal ct with dense v-networks[END_REF][START_REF] Ye | Multi-depth fusion network for whole-heart ct image segmentation[END_REF].

2.3/ CONCLUSION

According to the cardiac activity to visualize, several imaging modalities can be utilized to image the heart. For instance, infarcted tissues can be observed using MRI and gadolinium-contrast agents to help therapy planning. In the clinical diagnostic process, segmentation of the heart is essential to yielding quantitative measurements. Neverthe- i.e., visual elements in images. The convolved outputs are then passed per a batch normalization (BN) [START_REF] Ioffe | Batch normalization: Accelerating deep network training by reducing internal covariate shift[END_REF] followed by a nonlinear activation function to perform feature maps extractions, which are later downsampled by pooling layers to capture an increasingly significant field of vision. Fully connected layers are then used to decrease features' dimensions and achieve high-level reasoning [START_REF] Hinton | Improving neural networks by preventing coadaptation of feature detectors[END_REF][START_REF] Simonyan | Very deep convolutional networks for largescale image recognition[END_REF][START_REF] Ioffe | Batch normalization: Accelerating deep network training by reducing internal covariate shift[END_REF]. The final output is a fix-dimension vector which each element's form depends on the specific task (object localization, image classification, regression, patch-based segmentation, etc.). Through increasing depth (stacking many hidden layers), accuracy might be improved to a great extent or might not, depending on the complexity of the task.

Figure 3.1: Architecture of a CNN. The network's input is a CMR image containing three building blocks: convolutional, pooling, and fully connected layers [START_REF] Chen | Deep learning for cardiac image segmentation: a review[END_REF].

CNNs can be used for image segmentation tasks [START_REF] Zhou | Threedimensional ct image segmentation by combining 2d fully convolutional network with 3d majority voting[END_REF][START_REF] Kleesiek | Ilastik for multi-modal brain tumor segmentation[END_REF][START_REF] Moeskops | Deep learning for multi-task medical image segmentation in multiple modalities[END_REF]. Nevertheless, this task needs to split every input image into patches and, after that, train a CNN to predict the class label of each patch's center pixel. A pixel-wise segmentation map is finally obtained for the entire image through forwarding patches at various positions into the CNN for classification. For reliable pixel-wise segmentation, a fully convolutional neural network (FCN) is more often employed. Many variants of FCNs have been introduced to enhance the segmentation accuracy.

The U-Net [START_REF] Ronneberger | Convolutional networks for biomedical image segmentation[END_REF], which will be discussed in the upcoming chapter, is the most widespread . The advance of U-Net has become a research hotspot in medical image segmentation.

Various cardiac image segmentation works in the literature were based on the U-Net and its 3D variants (the 3D U-Net [START_REF] ¸ic ¸ek | 3d u-net: learning dense volumetric segmentation from sparse annotation[END_REF] and the 3D V-Net [START_REF] Milletari | Fully convolutional neural networks for volumetric medical image segmentation[END_REF]), yielding outstanding segmentation accuracy for different cardiac segmentation applications [START_REF] Isensee | Automatic cardiac disease assessment on cine-mri via timeseries segmentation and domain specific features[END_REF][START_REF] Xia | Automatic 3d atrial segmentation from ge-mris using volumetric fully convolutional networks[END_REF][START_REF] Tao | Deep learning-based method for fully automatic quantification of left ventricle function from cine mr images: a multivendor, multicenter study[END_REF]. Based on FCNs, Badrinarayanan et al. [START_REF] Badrinarayanan | A deep convolutional encoder-decoder architecture for image segmentation[END_REF] proposed an encoder-decoder architecture, called

3.1.3/ RECURRENT NEURAL NETWORKS (RNNS)

Recurrent neural networks (RNNs) are artificial neural networks used for sequential data, such as ultrasound image sequences and cine MRI [START_REF] Van Zon | Automatic cardiac landmark localization by a recurrent neural network[END_REF]. As shown in Figure 3.5 RNNs retain previous outputs and use their internal state to decide when processing the next inputs. The fusion of RNN and 2D FCN is widely used in cardiac segmentation to refine the inter-slice coherence of generated segmentation [START_REF] Poudel | Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation[END_REF].

3.1.4/ AUTOENCODERS (AES)

Autoencoders (AEs) are unsupervised neural networks that aim to learn compact latent representations from input data. A classic autoencoder's architecture contains two main networks: an encoder network that compresses the input and produces the code and a decoder network to reconstruct the data back to the input dimension using this code (see Figure 3.6). As the learned representations include usually helpful information in the data, several researchers have used autoencoders to extract common semantic features and shape information from original data for cardiac image segmentation guidance [START_REF] Oktay | Multi-input cardiac image super-resolution using convolutional neural networks[END_REF][START_REF] Schlemper | Cardiac mr segmentation from undersampled kspace using deep latent representation learning[END_REF][START_REF] Yue | Cardiac segmentation from lge mri using deep neural network incorporating shape and spatial priors[END_REF]. Oktay et al. [START_REF] Oktay | Multi-input cardiac image super-resolution using convolutional neural networks[END_REF] developed a residual convolutional network-based model to reconstruct 3D volumes from the full stack of 2D images for better image analysis.

3.1.5/ GENERATIVE ADVERSARIAL NETWORKS (GANS)

Generative adversarial networks (GANs) are generative-based models proposed by Goodfellow et al. [START_REF] Goodfellow | Generative Adversarial Networks[END_REF] to create new, synthetic images. GANs learn to generate from a training process through two adversarial networks contesting with each other: a generator network and a discriminator network (see Figure 3.7a). The generator aims to artificially create fake images across a random noise that it received, while the discriminator is used to determine whether an image is "real". GANs are successfully applied to segmentation tasks (see Figure 3.7b). A segmentation network substitutes the generator. The discriminator is needed to identify the generated segmentation maps from the gold standard ones [START_REF] Luc | Semantic segmentation using adversarial networks[END_REF][START_REF] Savioli | A generative adversarial model for right ventricle segmentation[END_REF]. Lau et al. [START_REF] Lau | Scargan: chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans[END_REF] proposed ScarGAN network to simulate scar region on healthy MYO and artificially augment the training sets using chained GAN.

Training a U-Net with supplementary simulated scar tissue scans demonstrated more accurate segmentation on test images ("80.5%" vs. "75.9%").

3.1.6/ ADVANCED BUILDING MODULES FOR BETTER SEGMENTATION

Recently, several researchers have introduced advanced building modules to learn improved features for the accurate segmentation of images. These modules have been extensively used in previous neural networks to boost the performance of cardiac image segmentation. With this aim, we report in this section three different types of state-of-the art methods: a) advanced convolutional blocks for multiscale feature aggregation (e.g., deep supervision [START_REF] Lee | Deeply-supervised nets[END_REF], inception modules [START_REF] Szegedy | Going deeper with convolutions[END_REF], dilated convolutional kernels [START_REF] Yu | Multi-scale context aggregation by dilated convolutions[END_REF], atrous spatial pyramid pooling [START_REF] Chen | Rethinking atrous convolution for semantic image segmentation[END_REF]); b) adaptive convolutional kernels to capture most relevant

TRAINING NEURAL NETWORKS

features (e.g., attention units [START_REF] Vaswani | Attention is all you need[END_REF], squeeze-and-excitation blocks [START_REF] Jie | Squeeze-and-excitation networks[END_REF]); c) interlayer connections to recover previous features in the following layers (e.g., residual connections [START_REF] He | Deep residual learning for image recognition[END_REF], dense connections [START_REF] Huang | Densely connected convolutional networks[END_REF]).

3.2/ TRAINING NEURAL NETWORKS

The training process is the most challenging step on DL methods due to the computational and configuration intricacy required for the execution. This process needs a dataset including paired images and ground truths, an optimizer, and a loss function. The model training aims to get the best network parameters to reduce the loss function.

3.2.1/ GRADIENT DESCENT OPTIMIZER

A deep network contains millions of parameters representing a mathematical solution to such a task. The trained network is adapted to the learning process beneath a specific parameter set by optimizing many network attributes. In particular, gradient descent is an optimization algorithm to reduce the loss function. Different gradient descent optimizers have been designed (e.g., Momentum SGD [START_REF] Qian | On the momentum term in gradient descent learning algorithms[END_REF], AdaGrad [START_REF] Duchi | Adaptive subgradient methods for online learning and stochastic optimization[END_REF], Adam [START_REF] Kingma | A method for stochastic optimization[END_REF]).

3.2.2/ LOSS FUNCTIONS IN DL

During the training process, we reduce the network error through a loss function, which assesses how well the learning algorithm fits the dataset. To that end, a suitable loss function is highly required to design and configure the network.

Given a series of of paired images and labels {(x i , y i ) : i = 1, ..., N}, the network learns the mapping link of x → y. Thus it predicts the output (ŷ) the closest possible to the ground truth (y). In the following, we report different loss functions frequently used for regression, image classification, and segmentation.

Mean Squared Error Loss (MSE), also recognized L2 Loss is the default loss function

for regression tasks such as image reconstruction, heart localization. The MSE is defined as:

L MS E = 1 N N i=1 (y i -ŷi ) 2 (3.1)
where y i , ŷi denote the vectors of the gold standard and predicted values, N represents the number of dataset samples.

Cross-entropy (CE)

is the most used loss function for image classification and segmentation problems. In multi-class segmentation, and for every class, this loss resumes the pixel-wise probability errors between the actual gold standard map y and its relative predicted output p.

L CE = - 1 N N i=1 C c=1 y c i log(p c i ) (3.2)
where C represents the number of all categories.

Especially for image segmentation, several researchers used soft-Dice loss function [START_REF] Milletari | Fully convolutional neural networks for volumetric medical image segmentation[END_REF], that penalizes the dissimilarity between the actual gold standard map and its corresponding predicted segmentation at pixel-level:

L Dice = 1 - 2 N i=1 C c=1 y c i p c i N i=1 C c=1 (y c i + p c i ) (3.3)
Moreover, different variants of the cross-entropy and soft-Dice loss functions (e.g., the weighted cross-entropy loss [START_REF] Jang | Automatic segmentation of lv and rv in cardiac mri[END_REF][START_REF] Baumgartner | An exploration of 2d and 3d deep learning techniques for cardiac mr image seg-mentation[END_REF] and weighted soft-Dice loss [START_REF] Yang | Class-balanced deep neural network for automatic ventricular structure segmentation[END_REF][START_REF] Khened | Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers[END_REF]) are widely utilized to solve the class imbalance issues in medical image segmentation tasks in which the loss value is weighted to consider infrequent categories.

Several other DL networks use a hybrid loss that fused various loss functions (e.g., focal loss [START_REF] Chen | Fr-net: Focal loss constrained deep residual networks for segmentation of cardiac mri[END_REF], soft-Dice loss, and weighted cross-entropy) to alleviate the class imbalance problem, and so to boost the segmentation performance [START_REF] Yang | Hybrid loss guided convolutional networks for whole heart parsing[END_REF][START_REF] Ye | Multi-depth fusion network for whole-heart ct image segmentation[END_REF].

3.2.3/ REDUCE OVER-FITTING

Due to the limited training size datasets compared to the number of the learnable parameters in a deep model, over-fitting is the greatest challenge of training deep models (see Figure 3.8). Below, we review few methods overcoming this issue in the literature:

trained with limited size datasets

Weight initialization [START_REF] Glorot | Understanding the difficulty of training deep feedforward neural networks[END_REF][START_REF] Mishkin | All you need is a good init[END_REF][START_REF] He | Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[END_REF] is a crucial conception choice that aims to impede the outputs of layer activations from vanishing or exploding in the forward transfer process of DL networks.

Dropout [START_REF] Hinton | Improving neural networks by preventing coadaptation of feature detectors[END_REF][START_REF] Srivastava | Dropout: a simple way to prevent neural networks from overfitting[END_REF], a regularization method to avoid overfitting by randomly removing a fraction of nodes in DL networks over each training iteration, is amongst the widely used features for improving the network's performance.

Data augmentation [START_REF] Zhang | When unseen domain generalization is unnecessary? rethinking data augmentation[END_REF][START_REF] Chen | Improving the generalizability of convolutional neural network-based segmentation on cmr images[END_REF] is an effective technique used in model training, where it raises the number of input data by artificially generating training images through applying Figure 3.8: Illustration of over-fitting [START_REF] Kregnes | Myocardial segmentation in lge-cmr images using deep neural networks[END_REF].

an ensemble of affine transformations to existing data.

Transfer learning is a deep network method that aims to reuse a model pre-trained on existing large datasets, using its knowledge gained for this task. The model can rapidly converge even with restricted data. Several works have proved the potential of transfer learning in improving the network generalization capacity for cardiac ventricle segmentation [START_REF] Chen | Med3d: Transfer learning for 3d medical image analysis[END_REF][START_REF] Fahmy | Automated analysis of cardiovascular magnetic resonance myocardial native t 1 mapping images using fully convolutional neural networks[END_REF][START_REF] Khened | Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers[END_REF].

3.3/ EVALUATION METRICS

How to assess the efficiency of segmentation methods is a crucial issue. The validity of segmentation algorithms can be evaluated in many aspects, such as quantitative accuracy, inference time, and memory uses. Below we mainly outline the EMIDEC challenge's metrics2 for measuring the performance of segmentation algorithms. Region-based metrics and volume-based metrics were computed for each test subject. After that, we measured their mean values to evaluate the performance of our myocardial disease segmentation. Frequently used comparison measures to evaluate automatic segmentation, such as volumetric Dice similarity coefficient or Hausdorff distance, have proven to be good geometric similarity evaluation metrics. In the present task, the volumetric difference metric for evaluating 3D myocardial segmentation is calculated to visualize the volumewise performance of the approach. Though quantitative metrics are utilized to compare several methods on benchmarks (doctors' manual-contouring for medical segmentation), qualitative results are significant in concluding which technique is best.

Dice similarity coefficient (DSC) is a popular metric in validating medical image

segmentation (see Eq. 3.4). It is commonly used to compute the similarity between predicted and gold standard maps. Its value range is 0 (mismatch) to 1 (excellent match).

DS C = 2 |P ∩ G| |P| + |G| (3.4)
where P, G denote the predicted and gold standard maps, respectively. [START_REF] Menze | The multimodal brain tumor image segmentation benchmark (brats)[END_REF] calculates the degree of similarity amongst two sets of points: the distance between the two delineations of the gold standard and the predicted segmentation. It is a complementary statistic to the DSC. The HD identifies segmentations with wide local differences, although they were well segmented.

Hausdorff distance (HD)

A lower value of HD mirrors a higher segmentation performance. The metric is determined as follows:

HD = (max p i ∈P (min g j ∈G (d(p i , g j ))), max g i ∈G (min p j ∈P (d(p i , g j )))) (3.5)
where P = {p i : i = 1, ..., N P }, G = g j : j = 1, ..., N G denote the predicted and gold standard maps, respectively, d represents the distance between p i and g j .

Absolute volume difference (AVD) measures the difference average across a

whole set of slices between predicted V P and gold standard V G LV volumes.

Absolute volume difference rate according to the volume of the MYO (AVDR)

is computed as follows:

AV DR = AV D V MYO (3.6)
where Benchmarking datasets with assessment metrics are much required to compare new methods for MI segmentation with state-of-the-art. Thus, we conduct a brief coverage of popular datasets for the LV myocardial segmentation task.

AV D = |V P -V G |

4.1/ MEDICAL IMAGE SEGMENTATION

Medical image segmentation, dividing an image into several predefined sets of organs or diseased bodies from medical imaging modalities, is one of the grand challenges, in medical image analysis to assist doctors in diagnosing and make decisions significantly.

In the previous few years, many researchers have applied DL-based networks to medical image segmentation in the scar [START_REF] Zabihollahy | Myocardial scar segmentation from magnetic resonance images using convolutional neural network[END_REF][START_REF] Xu | Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture[END_REF][START_REF] Moccia | Development and testing of a deep learning-based strategy for scar segmentation on cmr-lge images[END_REF], pancreas [START_REF] Roth | Multi-level deep convolutional networks for automated pancreas segmentation[END_REF][START_REF] Fu | Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images[END_REF], atrial [START_REF] Chen | Multi-task learning for left atrial segmentation on ge-mri[END_REF][START_REF] Xiong | Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network[END_REF],

prostate [START_REF] Yu | Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images[END_REF], brain [START_REF] Menze | The multimodal brain tumor image segmentation benchmark (brats)[END_REF][START_REF] Pereira | Brain tumor segmentation using convolutional neural networks in mri images[END_REF][START_REF] Havaei | Brain tumor segmentation with deep neural networks[END_REF], lung [START_REF] Kalinovsky | Lung image ssgmentation using deep learning methods and convolutional neural networks[END_REF], and multi-organ [START_REF] Zhou | Deep learning of the sectional appearances of 3d ct images for anatomical structure segmentation based on an fcn voting method[END_REF][START_REF] Trullo | Joint segmentation of multiple thoracic organs in ct images with two collaborative deep architectures[END_REF]. Their automated segmentation accuracy has outperformed classical segmentation techniques, including thresholding [START_REF] Xu | Threshold-based level set method of image segmentation[END_REF], edge-based [START_REF] Cigla | Region-based image segmentation via graph cuts[END_REF], and region-based methods [START_REF] Wei-Hua | Medical images edge detection based on mathematical morphology[END_REF].

4.2/ COMMONLY DL ARCHITECTURES FOR MEDICAL IMAGE SEG-

4.2.2/ 2.5D APPROACHES

Several networks performing medical image segmentation are based on 2.5D approaches. These approaches reaped the benefits of 3D segmentation by integrating (partial) 3D information to improve segmentation, whereas sidestepping its high memory consumption challenges. One of the more frequent methods is to use an ensemble of 2D CNNs applied to three orthogonal views of many directions (i.e., axial, coronal, and sagittal views) [START_REF] Prasoon | Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network[END_REF]. Such a 2.5D approach has richer neighboring pixel spatial information with lower computational cost than 3D [START_REF] Zheng | 3-d consistent and robust segmentation of cardiac images by deep learning with spatial propagation[END_REF]. These studies demonstrated slightly improved accuracy than 2D. Since a 3D volume represents a stack of adjacent 2D slices (2D image with neighbor slices), other alternatives integrated neighbor slice information to adduce a 3D temporal context for better segmentation performance. For example, Ganaye et al. [START_REF] Ganaye | Removing segmentation inconsistencies with semi-supervised non-adjacency constraint[END_REF] incorporated neighboring slices to that of the central as different input channels. Some authors investigated a 2.5D design system combining 2D and 3D methods [START_REF] Yang | Autosegmentation for thoracic radiation treatment planning: a grand challenge at aapm 2017[END_REF][START_REF] Xue | A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain mri images[END_REF].

Zheng [START_REF] Zheng | Deep learning for robust segmentation and explainable analysis of 3d and dynamic cardiac images[END_REF]

4.2.3/ FULLY VOLUMETRIC APPROACHES (3D APPROACHES)

In this section, we will review two state-of-the-art networks based on 3D data processing.

Most of them expanded the core idea of 2D approaches and modified it to a higher dimensional space [START_REF] Dou | Automatic lesion detection with three-dimensional convolutional neural networks[END_REF][START_REF] Kopelowitz | Lung nodules detection and segmentation using 3d mask-rcnn[END_REF][START_REF] ¸ic ¸ek | 3d u-net: learning dense volumetric segmentation from sparse annotation[END_REF][START_REF] Milletari | Fully convolutional neural networks for volumetric medical image segmentation[END_REF]. Compared to 2D and 2.5D approaches, volumetric images may provide complete 3D information in different orientations rather than in one view or three orthogonal views. Nevertheless, one of the primary challenges of such 3D networks resides in their higher requirement of resource consumption, as a consequence of the increased size of the model parameter space, restricting their implementation. The advent of 3D U-Net is of great interest to process volumetric images. Many volumetric segmentation methods, including LV CMR delineation, re-used the 3D U-Net model [START_REF] Fahmy | Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: a multicenter multivendor study[END_REF][START_REF] Yang | Class-balanced deep neural network for automatic ventricular structure segmentation[END_REF][START_REF] Baumgartner | An exploration of 2d and 3d deep learning techniques for cardiac mr image seg-mentation[END_REF][START_REF] Isensee | Automatic cardiac disease assessment on cine-mri via timeseries segmentation and domain specific features[END_REF][START_REF] Patravali | 2d-3d fully convolutional neural networks for cardiac mr segmentation[END_REF]. For instance, Fahmy et al. [START_REF] Fahmy | Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: a multicenter multivendor study[END_REF] proposed a 3D U-Net-based model with a sliding window to process large input stacks for accurate LGE scar quantification.

The suggested method achieved consistent performance across diverse vendors. Yang et.al [START_REF] Yang | Class-balanced deep neural network for automatic ventricular structure segmentation[END_REF] developed a fully automatic network for ventricular structure segmentation.

Their network is similar to [START_REF] ¸ic ¸ek | 3d u-net: learning dense volumetric segmentation from sparse annotation[END_REF] with the benefit of having a smaller memory footprint. PReLU non-linearities [START_REF] He | Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[END_REF] were employed throughout the model. Similar to [START_REF] Ronneberger | Convolutional networks for biomedical image segmentation[END_REF], it propagated features extracted from the compression path to the decompression path to assemble fine-grained detail, yielding a two-channel volumetric segmentation at the last convolutional layer. Finally, the outputs were turned into probabilistic segmentation by using soft-max voxelwise. The experimental evaluation demonstrated that V-Net model reached good performances on the "PROMISE 2012" challenge test dataset [START_REF] Litjens | Evaluation of prostate segmentation algorithms for mri: the promise12 challenge[END_REF]. This method proved its capacity in cardiac image segmentation tasks [START_REF] Vesal | A multi-stage fully convolutional network for cardiac mri segmentation[END_REF][START_REF] Xia | Automatic 3d atrial segmentation from ge-mris using volumetric fully convolutional networks[END_REF]. For instance, Vesal et al. [START_REF] Vesal | A multi-stage fully convolutional network for cardiac mri segmentation[END_REF] performed extensive experiments and interesting metrics comparison against V-Net, 3D U-Net, and several variants of the latter, including a multi-stage approach, for a multi-class segmentation on the ACDC dataset. Based on dilated convolutions and residual connections in the analysis path, the proposed multi-stage network reached a DSC score of "0.928" for LV and "0.853" for MYO delineations, compared to "0.908" and "0.809" for the V-Net and "0.889" and "0.805" for the original 3D U-Net. Gibson et al. [START_REF] Gibson | Automatic multi-organ segmentation on abdominal ct with dense v-networks[END_REF] proposed deep-learningbased architecture, known as DenseVNet, with a larger receptive field to segment eight organs. Compared to [START_REF] Milletari | Fully convolutional neural networks for volumetric medical image segmentation[END_REF], DenseVNet achieved remarkably higher DSC scores for all organs (e.g., "0.84" vs. "0.72" for the gallbladder, "0.76" vs. "0.68" for the esophagus, "99.21%" vs. "0.71" for the pancreas).

4.3/ DL METHODS FOR CMRI SEGMENTATION

CMRI is used to accurately quantify cardiac disease (e.g., scars) and assess the heart's anatomy and function. At present, CMRI draws much attention in the cardiac analysis domain. In this section, we described several DL-based CMRI segmentation networks.

4.3.1/ VENTRICLE SEGMENTATION

As the LV plays a significant role in cardiac function assessment, many research studies have been made. We reviewed in Table 4.1 a list of bi-ventricle segmentation algorithms that have been evaluated on the ACDC dataset1 . Our comparison comprises challenge participants' techniques and three other segmentation models that have been developed

after the challenge: [START_REF] Zotti | Convolutional neural network with shape prior applied to cardiac mri segmentation[END_REF][START_REF] Li | Apcpnet: aggregated parallel cross-scale pyramid network for cmr segmentation[END_REF][START_REF] Painchaud | Cardiac mri segmentation with strong anatomical guarantees[END_REF]. As shown, the top networks are those built by Isensee et al. [START_REF] Isensee | Automatic cardiac disease assessment on cine-mri via timeseries segmentation and domain specific features[END_REF] and Li et al. [START_REF] Li | Apcpnet: aggregated parallel cross-scale pyramid network for cmr segmentation[END_REF]. For example, compared to the classical levelset technique [START_REF] Grinias | Fast fully-automatic cardiac segmentation in mri using mrf model optimization, substructures tracking and b-spline smoothing[END_REF], both models reached higher accuracy, highlighting the efficiency of DL-based models. volume, the primary disadvantage of applying 2D cardiac segmentation networks is that they work slice-by-slice without learning inter-slice dependencies. Thereby, 2D models may not be sufficient to segment the LV on slices where the boundaries of the LV are not greatly delineated. Multiple studies have leveraged further contextual information such as shape priors [START_REF] Zotti | Gridnet with automatic shape prior registration for automatic mri cardiac segmentation[END_REF][START_REF] Chen | Learning shape priors for robust cardiac mr segmentation from multi-view images[END_REF] to overcome this issue and thus improve 2D FCN segmentation.

Several other methodologies used RNNs, and multi-slice models (2.5D models) [START_REF] Poudel | Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation[END_REF][START_REF] Patravali | 2d-3d fully convolutional neural networks for cardiac mr segmentation[END_REF][START_REF] Zheng | 3-d consistent and robust segmentation of cardiac images by deep learning with spatial propagation[END_REF][START_REF] Du | Cardiac-deepied: automatic pixel-level deep segmentation for cardiac bi-ventricle using improved end-toend encoder-decoder network[END_REF] to introduce spatial constraints, improving the segmentation of stack of slices.

These models are also used to highlight Spatio-temporal information over the cardiac cycle's frames for better segmentation performances [START_REF] Wolterink | Automatic segmentation and disease classification using cardiac cine mr images[END_REF][START_REF] Savioli | A generative adversarial model for right ventricle segmentation[END_REF][START_REF] Yan | Left ventricle segmentation via optical-flow-net from short-axis cine mri: preserving the temporal coherence of cardiac motion[END_REF][START_REF] Qin | Joint learning of motion estimation and segmentation for cardiac mr image sequences[END_REF][START_REF] Du | Cardiac-deepied: automatic pixel-level deep segmentation for cardiac bi-ventricle using improved end-toend encoder-decoder network[END_REF].

4.3.1.3/ ANATOMICAL CONSTRAINTS

The training process using only standard loss functions may fail to extract relevant anatomical structures' features. Thus, different works explored the benefit of integrating anatomical constraints at the training step for a perfect model prediction. These constraining, represented as regularization terms to consider prior knowledge (e.g., topology [START_REF] Clough | Explicit topological priors for deep-learning based image segmentation using persistent homology[END_REF],

contour [START_REF] Chen | Learning shape priors for robust cardiac mr segmentation from multi-view images[END_REF], and shape [START_REF] Oktay | Anatomically constrained neural networks (acnns): application to cardiac image enhancement and segmentation[END_REF][START_REF] Zotti | Convolutional neural network with shape prior applied to cardiac mri segmentation[END_REF][START_REF] Yue | Cardiac segmentation from lge mri using deep neural network incorporating shape and spatial priors[END_REF]), force the model to produce more accurate segmentation results. For example, Oktay et al. [START_REF] Oktay | Anatomically constrained neural networks (acnns): application to cardiac image enhancement and segmentation[END_REF] proposed an ACNN model that embeds prior knowledge into CNNs-based segmentation through an autoencoder network. Its output is enforced to follow a non-linear compact representation of the underlying anatomy. LGE-MRI is often used to put in evidence MI regions and lesions assigned to other diseases for better management of scar [START_REF] Kim | Relationship of mri delayed contrast enhancement to irreversible injury, infarct age, and contractile function[END_REF]. Earlier scar segmentation was usually achieved using traditional techniques (e.g., thresholding and clustering techniques), responsive to the local intensity variations [START_REF] Zabihollahy | Myocardial scar segmentation from magnetic resonance images using convolutional neural network[END_REF]. The primary drawback is its need for manual delineation of the ROI, reducing the computational costs [START_REF] Carminati | Comparison of image processing techniques for nonviable tissue quantification in late gadolinium enhancement cardiac magnetic resonance images[END_REF]. These techniques are not relevant for advanced research works and clinical applications.

In recent studies, DL networks have been fused with classical segmentation tools to segment scar regions. For example, the authors in [START_REF] Yang | Segmenting atrial fibrosis from late gadolinium-enhanced cardiac mri by deep-learned features with stacked sparse auto-encoders[END_REF] employed an atlas-based technique for LA identification and a DL model for fibrotic tissue detection in that area. Chen et al. [START_REF] Chen | Multiview two-task recursive attention model for left atrium and atrial scars segmentation[END_REF] used an end-to-end DL method for LA and atrial scars segmentation. In this method, a multi-view CNN with a recursive attention block is proposed to improve the segmentation performance by combining features from different views.

Segmentations of MYO lesions, particularly MI and MVO delineations algorithms involving DL, are less common and more recent. Motivated by the success of DL, Fahmy et al.

[133] adopted a U-Net architecture for segmenting LV MYO and scars from LGE-MRI obtained from subjects with heart disease. Moccia et al. [START_REF] Moccia | Development and testing of a deep learning-based strategy for scar segmentation on cmr-lge images[END_REF], and Zabihollahy et al. [START_REF] Zabihollahy | Myocardial scar segmentation from magnetic resonance images using convolutional neural network[END_REF] applied a semi-automated technique that needs manual delineation of the MYO and then Camarasa et al. [START_REF] Camarasa | Uncertainty-based segmentation of myocardial infarction areas on cardiac mr images[END_REF] proposed two approaches to assess if the uncertainty of an auxiliary unsupervised task is helpful for myocardial infarction segmentation. As illustrated in Zhang [START_REF] Zhang | Cascaded convolutional neural network for automatic myocardial infarction segmentation from delayed-enhancement cardiac mri[END_REF] proposed a cascaded convolutional neural network to segment myocardial areas from LGE-MRI automatically. Its method achieved the best segmentation performance. As shown in Figure 4.9, the winner first employed 2D U-Net to focus on the intra-slice information for a preliminary segmentation, and then a 3D U-Net to use the to interpolate two images and their corresponding segmentation maps.

4.4/ EXISTING CMRI DATASETS FOR THE TASK

The heterogeneous aspect of the tissue of interest (huge size, shape, and location variations from one subject to another [START_REF] Kamnitsas | Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation[END_REF]) is one of the main challenges in medical image segmentation. In addition, the requirement for a proper dataset is still critical to reaching coherent segmentation results. Thus, some prevalent challenges are established to achieve accurate LV segmentation and benchmark several CMR segmentation networks. The Sunnybrook Cardiac Data (SCD)2 contains 45 cardiac cine MRI images from a fusion group of subjects and several pathologies: hypertrophy cardiomyopathy, HF with infarction, HF without infarction, and healthy. The MRI images are provided with a set of hand-drawn delineations, given in text files, for both endocardium and epicardium [START_REF] Radau | Evaluation framework for algorithms segmenting short axis cardiac mri[END_REF].

The data were acquired with a 1.5T GE Signa MRI and temporal resolution of 20 cardiac phases for 10-15 second breath-holds. Recent works [START_REF] Queir Ós | Fast automatic myocardial segmentation in 4d cine cmr datasets[END_REF][START_REF] Avendi | A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri[END_REF][START_REF] Tan | Cardiac left ventricle segmentation using convolutional neural network regression[END_REF] reported several segmentation approaches' results achieved since the SCD challenge. This challenge3 highlights LV segmentation approaches' performances [START_REF] Suinesiaputra | A collaborative resource to build consensus for automated left ventricular segmentation of cardiac mr images[END_REF]. This survey generated consensus segmentation images based on the STAPLE algorithm [START_REF] Warfield | Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation[END_REF] using three semi-and two fully automated networks of contributing participants (raters).

4.4.3/ VENTRICULAR INFARCT SEGMENTATION CHALLENGE (LIVSCAR), MIC-CAI 2012

The LivScar dataset is presented in the review [START_REF] Karim | Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late gadolinium enhancement mr images[END_REF]. The database4 contains 30 LGE-MRI of humans (nh=15) and pigs (np=15), obtained from two imaging centers (see Fig-

4.4.4/ AUTOMATED CARDIAC DIAGNOSIS CHALLENGE (ACDC), MICCAI-STACOM 2017

The ACDC dataset [START_REF] Bernard | Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved[END_REF] was acquired in clinical routine at the University Hospital of Dijon

4.4.5/ AUTOMATIC EVALUATION OF MYOCARDIAL INFARCTION FROM DELAYED-ENHANCEMENT CMRI (EMIDEC) CHALLENGE, MICCAI-STACOM 2020

The EMIDEC dataset [START_REF] Lalande | Emidec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac mri[END_REF] comprises 150 clinical exams with LGE-MRI in SA view covering the LV from healthy subjects (nh=50) or cases with MI (ni=100), using a NIfTI format, and their respective myocardial annotations (i.e., the MYO, MI, and MVO). The EMIDEC The number of slices goes from 5 to 10 per exam. EMIDEC challenge presents these principal issues: data imbalance, low contrast, heterogeneous appearances of diseased regions, and various SA views. To the best of our knowledge, the EMIDEC challenge is the first one that provided annotated data and clinical information for improving methods' classification and segmentation.

4.4.6/ OTHER DATASETS

To evaluate performances of CMRI segmentation approaches, other international challenges, providing LGE-MRI and manual delineations, have been organized over the MIC-CAI conference (e.g., the MS-CMRSeg challenge 6 and the MyoPS challenge 7 ). Although, their subjects' number was inferior to that in the EMIDEC dataset. Visualized samples in both segmentation tasks are shown in

5.2/ DATA PROCESSING

In this work, the EMIDEC dataset was used for assessment purposes. This section will depict the data processing (i.e., pre-processing including data dimension choice and postprocessing methods). It is a necessary stage in DL-based image segmentation.

5.2.1/ PRE-PROCESSING

Data pre-processing task was performed before models training and testing to efficiently prepare the data for the following myocardial segmentation step. Particularly, target im-CHAPTER 5. PROPOSED METHODOLOGY ages and their associated labels were cropped to a normalized set whose center was the centroid of the LV cavity tissue to reduce the processing area. A reduction of the original data dimension is required to remove unnecessary information and accelerate the segmentation processing. An automatic crop function is proposed to determine a sub-volume containing myocardial tissues in all SA slices from the LGE-MRI.

Since EMIDEC data size varied from subject to subject, cropped LGE-MRI sizes were standardized. To this end, all MR volumes were resized to 96 × 96 × 16 pixels 3 volume size (i.e., the best minimum crop size found) through adding empty slices [START_REF] Qayyum | Automatic segmentation of tumors and affected organs in the abdomen using a 3d hybrid model for computed tomography imaging[END_REF].

The resulting images were further processed through standard adaptive histogram equalization technique to enhance the image quality [5,[START_REF] Xie | 3d size and shape characterization of natural sand particles using 2d image analysis[END_REF] and a non-local mean denoising to smooth the image [START_REF] Buades | A non-local algorithm for image denoising[END_REF]. The preprocessed image results can be observed in Figure 5.1.

Middle Apex

5.2.2/ POST-PROCESSING

Post-processing was required as the last stage in the segmentation pipeline for an accurate smoothing of prediction contours while removing noisy predicted tissues. This computational step will improve the quantification performance, including false-positive reduction. A mathematical morphology image opening operation with a kernel size of 3 × 3 is performed to exclude any isolated or small predicted segments with a minimum setting size of 64 voxels from the segmentation result. We also used connected components for better segmentation of diseased regions in the whole slices in LGE-MRI stacks.

A majority voting technique based on all fusions of segmentation maps, obtained with varying training parameters, is required to increase sensitivity for segmenting myocardial tissues, yielding the desired results. For example, the voxel was labeled as MI if at least three out of the proposed models' outputs predicted this voxel as an MI label. The final segmentation network (or ensemble) that achieves the highest DSC on the training set is automatically chosen. In the last step, labeled cropped slices were resized to the original volume size.

5.3/ NETWORK ARCHITECTURES

This section presents an end-to-end overview of our proposed models. We achieved the myocardium quantification issue using three approaches: the 2.5D SegU-Net framework and 3D constrained frameworks based on volumetric U-Net architecture. Significant reasons motivated the choice of these approaches. As reported in the previous chapters, 2D U-Net, 3D U-Net, and 2.5D methods achieved outstanding performance in medical segmentation. Several authors have also introduced anatomical constraints, contextual information, and advanced building blocks such as inception modules and atrous spatial pyramid pooling to improve image segmentation. The first network we proposed for the myocardial segmentation issue is called 2.5D SegU-Net. The algorithm is a hybrid of SegNet [START_REF] Badrinarayanan | A deep convolutional encoder-decoder architecture for image segmentation[END_REF], and U-Net [START_REF] Ronneberger | Convolutional networks for biomedical image segmentation[END_REF] networks. The schematic view of our approach can be seen in Figure 5. anatomical and pathological networks. As mentioned above, we first crop the ROI. An anatomical network is then applied to the ROI images to segment the LV cavity and the MYO regions. Finally, a pathological network is proposed to identify damaged areas (scar and MVO) from MYO segmented tissues. The red, green, blue, and yellow colors denote the LV cavity, the MYO, scar, and MVO, respectively.

5.3.1/ ALGORITHM 1: SEGU-NET

5.3.2.1/ ANATOMICAL NETWORK

The proposed anatomical model is built based on an encoder-decoder architecture. We have introduced the proposed CBAM in the encoding path and the proposed EDP block in the decoding path. A modified attention module has been used in concatenated shortcut connections to pass contextual and positional features from the contracting path to the extracting path.

The features maps are aggregated from several branches using kernels of different sizes in the inception residual block. The residual connections provide smooth learning regarding the layer inputs, rather than learning an unreferenced function [START_REF] Szegedy | Going deeper with convolutions[END_REF]. The proposed anatomical network is shown in Figure 5 designates the BN layer, and 1 × 1 Conv denotes the bottleneck layer. The output of each inception residual block is summarized in Eq. 5.1.

x l+1 = c 1×1 (c 1×1 (x l )).c b (c 3×3 (c 1×1 (x l ))).c b (c 3×3 (c b (c 3×3 (c 1×1 (x l ))))).c b (c 5×5 (c b (c 5×5 (c 1×1 (x l ))))) + x l
(5.1) In segmentation, we used the Jaccard loss function, which is a metric-inspired loss based on a Jaccard score, computing the overlap between two objects [START_REF] Yuan | Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance[END_REF]. A weighted Jaccard (wIOU) and a mean weighted Jaccard (mwIOU) are two loss functions using the class weights and the weight coefficient's ratio to the sum of the weight coefficients, respectively. To alleviate the class imbalance between relatively small segmentation categories and the extensive BG, we optimize the summation of a Jaccard-based (i.e., wIOU or mwIOU) loss and a shape constraint loss. In short, the following equation gives the final loss function used for training the proposed pathological network.

Input 1 × 1 Conv 1 × 1 Conv 1 × 1 Conv 1 × 1 Conv Max Pooling BN 1 × 1 Conv 1 × 3 Conv
L Final = L S eg + λ S R × L S R (5.2)
where L S eg represents the wIOU or mwIOU based loss function, λ S R denotes the regularization term and L S R indicates the L2 loss function that is defined in Frobenius norm Eq. 5.3. λ S R = 10 -2 was found to be the best choice.

L S R = n i=1 ||RP i -RG i || 2 F (5.3)
where n denotes the total number of training volumes, RG i is the reconstructed ground truth, RP i is the reconstructed predicted segmentation and ||.|| F representes the Frobenius norm of an m × n matrix. LGE test MRIs were passed to the trained network to produce the corresponding segmentation maps in the testing phase. Each stage's details are described below.

5.3.3/ ALGORITHM 3: ICPIU-NET

5.3.3.1/ ANATOMICAL NETWORK

The segmentation methods used in the proposed anatomical network are inspired by the state-of-the-art nnU-Net [START_REF] Isensee | nnu-net: a self-configuring method for deep learning-based biomedical image segmentation[END_REF] architecture with default settings. nnU-Net is a fully automatic segmentation network based on U-Nets [START_REF] Ronneberger | Convolutional networks for biomedical image segmentation[END_REF] structure. It adapts to any new challenge in the biomedical field. nnU-Net proved high-quality configurations on a wide diversity of datasets and target image properties [START_REF] Isensee | nnu-net: a self-configuring method for deep learning-based biomedical image segmentation[END_REF]. Similar to U-Net, a stack of convolutional layers between poolings in the down-sampling path and of deconvolution operations in the upsampling path is used. Nevertheless, it replaced ReLUs activation functions with leaky ReLUs (leakiness = 10 -2 ) and used instance normalization [START_REF] Ulyanov | Instance normalization: The missing ingredient for fast stylization[END_REF] instead of BN [START_REF] Ioffe | Batch normalization: Accelerating deep network training by reducing internal covariate shift[END_REF]. The operations are arranged as follows: convolution -instance norm -leaky ReLUs.

The downsampling is done using strided convolutions rather than max-pooling. nnU-Net ensembles 2D U-Net and 3D U-Net architectures. 2D U-Net trained whole slices to focus on intra-slice information. 3D U-Net is used to learn volumetric spatial features. Therefore, the cross-validation outputs automatically yield to the best-obtained ensemble to be used for the testing prediction.

The proposed anatomical network used 2D, 3D, and cascaded U-Net to overcome the practical shortcoming of a 3D U-Net network on datasets with huge-size images. In a cascaded U-Net architecture, we first trained a 3D U-Net on 3D downsampled images for a preliminary segmentation. The segmentation outputs are then upsampled to the original resolution and transferred to a second 3D U-Net architecture, trained on patches at full resolution for final anatomical segmentation.

The anatomical network has been implemented using the Pytorch source code 1 based on the nnU-Net architecture. To train our approach, we used a five-fold cross-validation and Adam optimizer with an initial learning rate of 3×10 -4 . The learning rate is decreased over the training process using a polynomial learning rate scheduler. The SA slice and volume inputs are provided for both 2D and 3D U-Nets, respectively. The sum of the Crossentropy loss (L CE ) and the Dice loss (L DICE ) is applied to train the proposed anatomical network, as the final loss function (Eq. 5.4).

L = L CE + L DICE (5.4)
The L DICE loss function is defined as follows:

L DICE = - 2 |K| k∈K i∈I u k i v k i i∈I u k i + i∈I v k i (5.5)
where u represents the softmax output of the proposed anatomical network and v refers to the one-hot encoding of the gold standard delineation drawn manually by the experts.

Both u and v have shape I × K with i ∈ I indicates the pixels' number in the training patch/batch and k ∈ K denotes the different categories.

5.3.3.2/ PATHOLOGICAL NETWORK

Our last pathological network extended and modified the pathological framework of SPIU-Net, integrating more prior knowledge for better segmentation performance. The training process took a total time of 314 mins for 200.000 iterations. As shown in Figure 5.12, we incorporated the inclusion (IC in cyan) and class constraints (CC in purple) modules, linked as an extended framework and to the bottom of the 3D U-Net architecture respectively, to improve the final prediction of myocardial disease segmentation. These constraints are proposed as auxiliary L IC and L CC loss terms for highlighting small categories tissue to aid the segmentation.

We trained the proposed 3D CVAE using an iterative optimization process with manual We also proposed a binary classification module to differentiate infarcted subjects from healthy cases. The proposed is introduced in the pathology segmentation process for constraining the predicted label in this known class.

Optimizing the appropriate loss function for accurate segmentation is critical in the training process, especially to alleviate the class imbalance issue. To this end, we trained our pathological network with a fusion of multi-class IOU loss L S eg [START_REF] Kervadec | Boundary loss for highly unbalanced segmentation[END_REF], inclusion constraint loss L IC , and a classification constraint loss L CC . This final loss function is summarized in Eq. 5.6.

L Final = L S eg + λ IC × L IC + λ CC × L CC (5.6)
where λ IC represents the inclusion constraint penalty-term, L IC denotes the L2 loss function which is defined in Frobenius norm (Eq. 5.7), λ CC indicates the class constraint penalty-term and L CC is the cross-entropy loss function. We regulate with λ IC and λ CC weights in the gap [10 -2 , 10 -1 ].

L IC = n i=1 ||RP i -RG i || 2 F (5.7)
where n indicates the total number of training data, RG i designates the reconstructed manual contouring, RP i denotes the reconstructed segmentation output and ||.|| F represents the Frobenius norm of an m × n matrix.

The multiclass L S eg loss function computes the overlap between two samples [START_REF] Yuan | Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance[END_REF] and is integrated into the pathological framework as follows:

L S eg = L IoU = 1
|C| c∈C i p ic × p * ic i p ic + p * ic -p ic × p * ic (5.8)
where p ic denotes the prediction score at position i for class c, and p * ic represents the ground truth distribution being a delta function at yi * , the true label.

5.4/ CONCLUSION

This chapter proposes three accurate myocardial segmentation models based on a U-Net architecture with introduced blocks. The next chapter focuses on results and extensive experiments on the EMIDEC dataset performed using the proposed networks to assess their efficiency to segment the whole myocardium.

EXPERIMENTAL RESULTS AND

DISCUSSION

T his chapter outlines the outputs of each segmentation algorithm depicted in chapter 5 to the expert references of EMIDEC datasets. Each algorithm segmentation output is assessed with standard evaluation metrics introduced in chapter 3.

6.1/ FINDING THE BEST MODEL

Quantifying pathological regions in the LV may have significant clinical involvements. This work proposes an evaluation framework for several methods that segment and quantify myocardial areas. To evaluate how our approaches handled the MYO and diseased regions, we compare their outputs against several network's outputs, submitted to respond to the EMIDEC challenge, and a reference standard annotation.

6.2/ NETWORK PERFORMANCE

In summary, in this section, we assessed the performance of three novel DL methods which have been proposed for automatic myocardial segmentation, as described in chapter 5.

6.2.1/ EXPERIMENT ONE -SEGU-NET SEGMENTATION

6.2.1.1/ ACCURACY OF INTRA-OBSERVER MANUAL MYOCARDIAL DELINEATION

The first proposal is achieved before the EMIDEC dataset has been publicly available.

Intra-observer variability of manual segmentation of both healthy and diseased myocardial contours in LGE-MRI is necessary to establish a gold standard for clinical diagnosing and the assessment of automatic segmentation methods. To assess, intra-observer variability, a subset of cases (in total, nc = 35 exams, ns = 210 slices) was fully re-contoured by the same expert. The agreements of the intra-observer for myocardial manual delineations in terms of IOU, Accuracy, and DSC metrics, are summarized in Table 6. 1, showing the relevance of an expert observer's manual masks to evaluate the myocardial segmentation approaches. For all considered metrics, estimation of BG, healthy and diseased myocardial regions were consistently efficient for reference two when compared against reference one (intra-observer DSC scores varied from "0.67561" to "0.99836" and Accuracy from "0.74108" to "0.99842"). Representative examples of the masks produced by the intra-observer manual segmentation and the output from the proposed network for test data are visualized in Figure 6.1.

The experimental result shows that the automated approach yielded good segmentation of myocardium tissue. As compared to the intra-observer ground truths, the proposed SegU-Net model can also accurately detect myocardial diseased areas. delineation. The detailed boxplot results show that our method segmentation reached accurate similarity to expert ground truth using DSC and IOU evaluation metrics.

6.2.1.3/ COMPARISON STUDY WITH RELATED WORK

We compared the proposed SegU-Net segmentation results with seven previous methods to assess its performance. Summary of the quantitative results presented in Table 6.7 demonstrates that our proposal performed better for all the three evaluation metrics: IOU, Accuracy, and DSC.

DATASET

For SPIU-Net model evaluation, the whole 100 LGE-MRI with published ground truths was randomly split into 5 non-overlapping cross-validation folds. We aggregated the val-idation sets from 5-fold cross-validation. Table 6.9 and Table 6.10 illustrate our segmentation results on validation sets experiment without using any auxiliary post-processing operations compared with the other two existing methods. The Averages (Avg) and Standard deviations (Std) of the evaluation metrics, presented in Table 6.9 demonstrate the efficiency of the proposed algorithm to segment anatomical structures and pathological tissues on LGE-MRI scans accurately. Quantitatively, the results show the performance of the SPIU-Net network despite the small number of training samples. The trained ICPIU-Net model was used to predict myocardial segmentation for each of the test volumes. We compared the segmentation results of our approach to other existing networks reported in section 4.3.2.2. We also compared the ICPIU-Net prediction results

to ground truths and [START_REF] Brahim | A 3d network based shape prior for automatic myocardial disease segmentation in delayed-enhancement mri[END_REF], which only enforces a shape prior information for segmenting damaged myocardial tissues. Both qualitative and quantitative results are described below.

LGE-MRI GT [211] ICPIU-Net 3D rendering

Subject 1 Bland-Altman plots of the proposed ICPIU-Net model vs. expert manual LV volumes are provided in Figure 6.10. In these graphs, the dashed blue line and red dashed lines illustrate the mean value of the difference, the upper and lower limits of accordance, respectively. Compared to expert annotations, our model's mean bias in evaluating MYO, infarct, and MVO volumes was "4.9888 cm 3 ", "1.2266 cm 3 ", and "0.5112 cm 3 ", respectively. The proposed ICPIU-Net network lightly overvalued myocardial region volumes producing a mean absolute LV volume error of "8.12%". In addition, a case-by-case study of our approach's generated segmentation demonstrated that the MI area could be accurately detected in 32 out of 33 pathological patients from the test dataset. The results

(a) (b) (c) (d) (e) Subject 2 (f) (g) (h) (i) (j) Subject 3 (k) (l) (m) (n) (o)

Base Middle Apex Base Middle Apex

LGE-MRI

(a) (b) (c) (d) (e) (f) GT (g) (h) (i) (j) (k) (l) ICPIU-Net (m) (n) (o) (p) (q) (r)
Figure 6.9: Examples of myocardial test segmentation compare results and the gold standard for three levels (base, middle, and apex) of two subject slices (columns 1-3 from subject 1 and columns 4-6 from subject 2). The proposed model is capable of segmenting the MYO while also identifying the small diseased areas. LV cavity is displayed in red, MYO in green, MI in blue, and MVO in Yellow.

prove the superiority of the ICPIU-Net network and the efficiency of each incorporated constraint information.

Table 6.14 outlines the quantitative results of our approach against state-of-the-art models on EMIDEC challenge for the testing dataset. Our proposed algorithm outperformed top-ranked frameworks achieving the best DSC, AVD, and AVDR results for diseased tissues segmentations. The second best DSC score was attained using [START_REF] Zhang | Cascaded convolutional neural network for automatic myocardial infarction segmentation from delayed-enhancement cardiac mri[END_REF] ("71.24%" for MI and "78.51%" for MVO). Our proposed model also yielded much better results in all metrics than several competing methods (DSC, AVD, and HD of "87.65%", "8863.41 mm 3 ", and "13.10 mm", respectively). The significant correlation between expert annotation and the proposed network demonstrated our approach's performance and clinical applicability for the automatic assessment of MI.

We conducted a comparative ablation study to assess the effect of adding prior constraint information to the segmentation loss. As can be seen from Table 6.15, the introducing of IC and CC regularization penalty terms into the baseline 3D U-Net improved the segmentation accuracy and thus provided more plausible segmentation close to the expert annotation. The experimental results revealed the pertinence of constraints modules to aid the myocardial segmentation.

The evaluation metrics (DSC, AVD, and AVDR) used for MVO segmentations may be in- consistent due to the small area of the MVO compared to the complete input LGE-MRI.

MVO absence on all the data may apparently provide correct results with DSC and vol- LGE-MRI has emerged as the gold standard for quantifying scar or MVO areas. It relies on the difference in signal intensity between normal myocardial area and infarcted tissue. We used the combination of the Jaccard loss and the Focal Loss as the final optimization loss function. Compared to the intra-observer variability, the proposed framework perfectly segments diseased myocardial regions of infarcted patients.

Our novel segmentation pipeline worked best in comparison with previous models.

• Secondly, we developed an SPIU-Net DL-based shape prior model for automatic MI segmentation from LGE-MRI. Furthermore, the proposed method aimed to detect MVO areas accurately. We first segmented the anatomical structures, i.e., the LV

Abstract:

Accurate myocardial segmentation in LGE-MRI is an important purpose for diagnosis assistance of infarcted patients. Nevertheless, manual delineation of target volumes is time-consuming and depends on intra-and inter-observer variability. This thesis aims at developing efficient deep learning-based methods for automatically segmenting myocardial tissues (healthy myocardium, myocardial infarction, and microvascular obstruction) on LGE-MRI. In this regard, we first proposed a 2.5D SegU-Net model based on a fusion framework (U-Net and SegNet) to learn different feature representations adaptively. Then, we extended to new 3D architectures to benefit from additional depth cues. In a second step, we proposed to segment the anatomical structures using inception residual block and convolutional block attention module and diseased regions using 3D Auto-encoder to perfect myocardial shape. To this end, a prior shape penalty term is added to 3D U-Net architecture. Finally, we proposed first segment the left ventricular cavity and the myocardium based on the no-new-U-Net and second use a priori inclusion and classification networks to maintain the topological constraints of pathological tissues within the pre-segmented myocardium. We have introduced a post-processing decision phase to reduce the uncertainty of the model. The stateof-the-art performance of the proposed methods is validated on the EMIDEC dataset, comprising 100 training images and 50 test images from healthy and infarcted patients. Comprehensive empirical evaluations show that all of our algorithms have promising results.

Titre : Architectures d'apprentissage profond pour la d étection automatique de segments myocardiques viables

Mots-cl és : Segmentation myocardique, LGE-IRM, Apprentissage profond, Forme ant érieure, Myocarde, Contraintes topologiques, Tissus pathologiques.

R ésum é :

La segmentation pr écise du myocarde en LGE-IRM est un objectif important pour l'aide au diagnostic des patients infarctus. N éanmoins, la d élimitation manuelle des volumes cibles prend du temps et d épend de la variabilit é intra-et inter-observateur. Cette th èse vise à d évelopper des m éthodes efficaces bas ées sur l'apprentissage profond pour segmenter automatiquement les tissus myocardiques (myocarde sain, infarctus du myocarde et obstruction microvasculaire) sur LGE-IRM. À cet égard, nous avons d'abord propos é un mod èle 2.5D SegU-Net bas é sur un cadre de fusion (U-Net et SegNet) pour apprendre diff érentes repr ésentations de caract éristiques de mani ère adaptative. Ensuite, nous avons étendu à de nouvelles architectures 3D pour b én éficier d'indices de profondeur suppl émentaires. Dans un deuxi ème temps, nous avons propos é de segmenter les structures anatomiques à l'aide du module d'attention du bloc r ésiduel initial et du bloc convolutif et des r égions malades à l'aide de l'auto-encodeur 3D pour perfectionner la forme du myocarde. A cet effet, un terme de p énalit é de forme pr éalable est ajout é à l'architecture 3D U-Net. Enfin, nous avons propos é dans un premier temps de segmenter la cavit é ventriculaire gauche et le myocarde sur la base du no-new-U-Net et dans un second temps d'utiliser des r éseaux d'inclusion et de classification a priori pour maintenir les contraintes topologiques des tissus pathologiques au sein du myocarde pr ésegment é. Nous avons introduit une phase de d écision post-traitement pour r éduire l'incertitude du mod èle. Les performances de pointe des m éthodes propos ées sont valid ées sur l'ensemble de donn ées EMIDEC, comprenant 100 images d'entraînement et 50 images de test de patients sains et infarctus. Des évaluations empiriques compl ètes montrent que tous nos algorithmes ont des r ésultats prometteurs.

1. 2 .

 2 GOALS AND ORIGINAL CONTRIBUTIONS utes or 10 minutes following gadolinium injection.

Figure 1 . 2 :

 12 Figure 1.2: Short-axis view of early and late cardiac LGE-MRI in a subject with AMI.The MI occurs hyperintense on the late LGE image, with a subendocardial region (black arrow) corresponding to MVO. On the early LGE sequence, the MYO is still saturated with gadolinium, although the MVO already occurs hypointense (black arrow)[START_REF] Bouleti | The no-reflow phenomenon: state of the art[END_REF].

Figure 1 . 3 :

 13 Figure 1.3: Short-axis LGE-MR images show left ventricular cavity (red), healthy MYO (green), myocardial scarring (blue), and MVO (yellow). The slices were extracted and cropped from the EMIDEC dataset (see section 4.4.5).

1. 4 .

 4 PUBLICATIONS ENSUING FROM THE THESIS 1.4/ PUBLICATIONS ENSUING FROM THE THESIS The Ph.D. research led to 3 journal papers (1 accepted, 1 subject to major revisions, and 1 submitted), 2 conference papers, and 1 workshop paper.1.4.1/ JOURNAL ARTICLES • Brahim, K., Qayyum, A., Lalande, A., Boucher, A., Sakly, A., and Meriaudeau, F.: A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI. IRBM. (2021) [211]. -1st Author. • Lalande, A. et al.: Deep learning methods for automatic segmentation of delayed enhancement-MRI. The results of the EMIDEC challenge. Medical Image Analysis. -Contributing. • Brahim, K., Arega T.W, Boucher, A., Bricq, S., Sakly, A., and Meriaudeau, F.: An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Priors Information U-Net (ICPIU-Net). Computer Methods and Programs in Biomedicine.

Figure 2 . 1 :

 21 Figure 2.1: Anatomy of the heart. 1

Figure 2 . 2 :

 22 Figure 2.2: Overview of the cardiac cycle showing all phases and relative mechanical changes. Relationship to the ECG is revealed as color red in black segments.2

Figure 2 . 3 : 1 . 2 .

 2312 Figure 2.3: CMRI at end-diastole (left) and end-systole (right) [38].

  ping sequences techniques have been progressively incorporated into CMR imaging settings, yielding accurate tissue characterization. Standardized cardiac T1 mapping is an MRI technique performed using balanced Steady-State Free Precession (bSSFP) sequences to compute a definite tissue's T1 (spin-lattice or longitudinal relaxation) time and visualize them voxel-vice on a parametric T1 map. It is a highly reproducible index that offers necessary measurements reflecting main myocardial properties [104]. T2 mapping mainly provides visualization and reliable quantification of myocardial edema and is thus a focus of several research tasks [29, 51]. It is more advantageous than other modalities in subjects with reduced LV function [104]. Similarly, T2 maps are acquired from a signal intensity versus time curve based on several spin-spin (transverse) relaxation times. Native T1, T2, and ExtraCellular Volume fraction (ECV) values, predictive parameters for diagnosis and treatment monitoring of cardiovascular diseases, are shown in Figure 2.4. Recently, several approaches used deep neural networks for CMRI segmentation (see section 4.3).

Figure 2 . 4 :

 24 Figure 2.4: Short-axis images of native T1 (T1 time computed in the absence of a contrast agent), T2, and ECV maps of control subject [100].

Figure 2 . 5 :

 25 Figure 2.5: Cardiac axis imaging planes, depending on thorax planes, (A) Coronal, (B), Sagital, and (C) Axial. (D) Vertical long axis (VLA) localizer and Horizontal long axis planning (HLA). (E) HLA localizer and short-axis planning (SA). (F) VLA localizer and SA planning. (G) SA plane is prescribed perpendicular to both the VLA and HLA [34].

Figure 2 . 6 :

 26 Figure 2.6: CMRI. a) Cine MRI HLA. b) Cine MRI SA [73].

Figure 2 . 7 :

 27 Figure 2.7: Example of an LGE-T1 slice in a patient with septal ischemia (arrow) using magnitude IR (left) against a second one using Phase-Sensitive IR (right).3

Figure 3 . 2 :

 32 Figure 3.2: Architecture of fully convolutional neural networks (FCN) for image segmentation. Acronyms: ReLU -Rectified Linear Unit; MVN -Mean-Variance Normalization [86]

Figure 3 . 3 :

 33 Figure 3.3: Overview of the U-Net network structure. The LV cavity is displayed in blue, the MYO in green, and the RV in red. 1

  SegNet for image segmentation. Whereas SegNet passes max-pooling indices to the upsampling layers, FCN learns deconvolution filters to upsample (i.e., appending the relative feature map from the encoder phase) (see Figure3.4). As detailed in chapter 5, the current work is based on 3D U-Net (SPIU-Net, ICPIU-Net) and a hybrid architecture of U-Net and SegNet, which takes the best of both models (SegU-Net).

Figure 3 . 4 :

 34 Figure 3.4: Overview of the SegNet architecture [90].

Figure 3 . 5 :

 35 Figure 3.5: Overview of the RFCN network for cardiac image segmentation. The orange arrow represents a recurrent connection to manage inter-slice dependencies learned via GRU. The network aims to segment cardiac ventricles from 2D CMRI [83].

Figure 3 . 6 :

 36 Figure 3.6:A typical structure of an autoencoder. An autoencoder uses an encoderdecoder framework, where the encoder maps the input into a lower-dimensional latentspace representation and then the decoder interprets this code to reconstruct the original data[START_REF] Chen | Deep learning for cardiac image segmentation: a review[END_REF].

Figure 3

 3 Figure 3.7: a) Chart of GANs framework for cardiac image synthesis. b) Schematic of adversarial training for cardiac image segmentation [195].

  and V MYO is the MYO volume of the gold standard annotation.3.4/ CONCLUSIONDL has been the research boom and the development engine of the image area. This chapter summarizes different neural networks and several methods for model training.Then, a brief overview of evaluation metrics for image segmentation is provided. The next chapter presents a detailed literature review of the state-of-the-art medical image segmentation, including scar segmentation approaches and benchmarking CMRI datasets.

4 STATE-

 4 OF-THE-ART T his chapter reviews a summary of DL-based methods, highlighting their contributions to network design. Several recent studies based on DL have demonstrated excellent performances in segmenting particular tissues such as the LV, scars, and coronary vessels, aiding follow-up quantitative analysis of cardiovascular anatomy and function.

MENTATION 4 . 2 . 1 /

 421 2D U-NETBased on FCN architecture, Ronneberger et al.[START_REF] Ronneberger | Convolutional networks for biomedical image segmentation[END_REF] proposed a U-Net network for biomedical image segmentation. This model comprises a U channel, composed of two paths of analysis (contracting) and synthesis (expansive), and skip-connections linking CHAPTER 4. STATE-OF-THE-ART the layers of the contracting path to their counterparts of the expansive one to provide them crucial high-resolution features. The network architecture uses two 3 × 3 convolutions followed by a ReLU activation function and a max-pooling operation to reduce the size of the latent image. The process is repeated until attaining a single features vector, useful to reconstruct an image with probability-wise classified pixels for accurate segmentation by incorporating transmitted outputs during the analysis path. The proposed network simultaneously fuses low-level feature maps, for better accuracy, with high-level feature maps, for complex features extraction. The U-Net architecture is shown in Figure 4.1.

Figure 4 . 1 :

 41 Figure 4.1: Original U-Net implementation. Skip-connections are represented by horizontal arrows [64].

Figures 4 .Figure 4 . 2 :Figure 4 . 3 :

 44243 Figures 4.2 and 4.3 show resultant myocardial segmentation of the U-Net-based architecture in native and contrast-enhanced T1-maps, respectively.

4. 2 . 3 . 1 /

 231 3D U-NET One of the most famous variants of U-Net architecture to reinforce it with richer spatial coherency information is 3D U-Net, proposed by Cicek et al.[START_REF] ¸ic ¸ek | 3d u-net: learning dense volumetric segmentation from sparse annotation[END_REF]. The authors extended the U-Net architecture via substituting 2D operations with their volumetrics counterparts.

Figure 4 .

 4 Figure 4.4 provides a schematic representation of 3D U-Net. The proposed algorithm provided dense volumetric segmentation from learned sparse 2D annotations. It densely segmented new data. Extensive results demonstrated its efficiency on a complex and

Figure 4 . 4 :

 44 Figure 4.4: Schematic representation of 3D U-Net architecture [70].

Figure 4 . 5 :

 45 Figure 4.5: Original structure of V-Net [78].

Table 4 . 2 :

 42 used a 2D network to discriminate scars from healthy MYO. Nowadays, fully automated infarction segmentation is still a challenging task. Xu et al.[START_REF] Xu | Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture[END_REF] proposed an RNN which learns motion patterns for automatic MI segmentation from cine MRI sequences without the injection of contrast agents. Compared to the ground truth segmentation on LGE-MRI, this network achieved a great overall Dice score. De La Rosa et al.[START_REF] De La | Myocardial infarction quantification from late gadolinium enhancement mri using top-hat transforms and neural networks[END_REF] proposed an automatic network for scar segmentation, including MVO regions from LGE-MRI. Their methodology is derived from VGG19 and consisted of two blocks. Firstly, healthy and diseased scans are distinguished using a classifier. Then, the MI is segmented by an initial fast coarse segmentation which is further refined by a boundary reclassification strategy.The proposed network reached state-of-the-art segmentation performances. 4.3.2.2/ EMIDEC SEGMENTATION NETWORKS EMIDEC segmentation aims to automatically segment the MYO and the diseased regions on each slice of LGE-MR volume. As shown in Table 4.2, many challengers proposed two-stage networks: a) delineation of the MYO (in its first stage); b) segmentation of the myocardial diseases around the vicinity of the MYO region (in its second stage). However, some others developed one-stage networks to perform a segmentation of all the tissues of interest in a single step. Main concepts of EMIDEC challenge methods. Contest Methods Description EMIDEC Segmentation Camarasa et al. a [193] MYO: 3D U-Net variant Scar: 3D U-Net variant Feng et al. b [198] 2D U-Net with dilated convolution Girum et al. a [199] MYO: 2D U-Net with SE block Scar: 2 U-Net with SE block Huellebrand et al. a [201] MYO: 2D U-Net variant Scar: Mixture model Yang and Wang b [208] 2D U-Net with SE and SK blocks Zhang a [209] MYO: 2D U-Net variant Scar: 3D U-Net variant Zhou et al. b [210] 2D U-Net with Attention a Two-stages model, b One-stage model.

Figure 4 . 6 ,

 46 their Baseline method first determined the ROI centered on the non-background labels to then used U-Net architecture to segment all myocardial regions from the definite ROI. Similar to the baseline segmentation, the Uncertainty-based method first localized the ROI. Then, a probabilistic Auto-Encoder is employed to provide an uncertainty map relative to ROI reconstruction. An uncertainty-based U-Net used the generated uncertainty map for myocardial delineations.Feng et al.[START_REF] Feng | Automatic scar segmentation from de-mri using 2d dilated unet with rotation-based augmentation[END_REF] proposed an automatic LGE-MRI segmentation model using: a) rotationbased augmentation to force the model to eliminate the image orientation and learn the anatomical and contrast relationships; b) dilated 2D U-Net to increase the robustness of the network against several slices' misalignment. The authors used the weighted crossentropy and soft-Dice loss functions to alleviate the class imbalance issue. They also favored slices containing myocardial disease areas, which existed in few cases, particularly the MVO. Girum et al.[START_REF] Girum | Automatic myocardial infarction evaluation from delayedenhancement cardiac mri using deep convolutional networks[END_REF] developed a two-stage CNN network to segment the anatomical structures firstly and then pathological areas from LGE-MRI. The segmented MYO tissue from the anatomical network is further employed to refine the pathological network's segmentation, thus produce the final four-class segmentation result (see Figure 4.7).

Figure 4 . 6 :

 46 Figure 4.6: Overview of the segmentation network[START_REF] Camarasa | Uncertainty-based segmentation of myocardial infarction areas on cardiac mr images[END_REF].

Figure 4 . 7 :

 47 Figure 4.7: Schematic representation of the segmentation network[START_REF] Girum | Automatic myocardial infarction evaluation from delayedenhancement cardiac mri using deep convolutional networks[END_REF].

Figure 4 .

 4 [START_REF] Qian | On the momentum term in gradient descent learning algorithms[END_REF] shows category and segmentation labels and hybrid branches that are used to supervise the complete segmentation process.

Figure 4 . 8 :

 48 Figure 4.8: Overall architecture of the segmentation network [208].

Figure 4 . 9 :

 49 Figure 4.9: Framework of the cascaded convolutional neural network [208].

Figure 4 . 10 :

 410 Figure 4.10: Schematic representation of anatomy prior based U-Net architecture [210].

4. 4 . 2 /

 42 LV SEGMENTATION CHALLENGE (LVSC), MICCAI-STACOM 2011The database comprises CMR acquisitions in long axis and SA views from 200 subjects with coronary artery disease and prior MI. Binary segmentation masks of the MYO for the 100 training subjects were provided. It is made disposable via the Cardiac Atlas Project[START_REF] Fonseca | The cardiac atlas project-an imaging database for computational modeling and statistical atlases of the heart[END_REF]. Multiple MRI scanners were used: GE Medical Systems (Signa 1.5T), Philips Medical Systems (Achieva 1.5T, 3T, and Intera 1.5T), and Siemens (Avanto 1.5T, Espree 1.5T, and Symphony 1.5T). The data are a clinically significant subject group as mass and volume are fundamental diagnostic indicators of adverse cardiac remodeling.

ure 4 .

 4 [START_REF] Simonetti | An improved mr imaging technique for the visualization of myocardial infarction[END_REF]. A myocardial mask, manually drawn by an expert observer using SA view slices, was publicly available. The human and porcine data were acquired with a 1.5T MR unit (Philips Achieva, The Netherlands) and 3T MRI unit (Siemens Healthcare, Germany), respectively. An exciting characteristic of this dataset is its heterogeneous image quality, which is helpful to perform the robustness of a network.

Figure 4 .

 4 Figure 4.11: A sample of LivScar dataset. (top-row) Human; (bottom-row) Porcine [75].

(

  France) with two MRI scanners of magnetic strengths ((1.5T -Siemens Area and 3T -Siemens Trio Tim), Siemens Medical Solutions, Germany). The expert annotations are manually delineated 3D volumes of the MYO, the LV and RV cavities, at end-systolic and end-diastolic slices. A set of SA view slices cover the LV, with a thickness from 5 mm to 10 mm and at times an interslice gap of 5 mm. The data coat different pathologies with enough patients to efficiently train deep-based networks and evaluate the primary physiological parameters' variability acquired from cine MRI. The dataset includes SA view MRI for 150 subjects evenly contains five classes: (NOR: patients with normal cardiac anatomy, MINF: patients with MI, DCM: patients with dilated cardiomyopathy, HCM: patients with hypertrophic cardiomyopathy, and ARV: patients with abnormal RV). Figure 4.12 presents a visualized example.

Figure 4 . 12 :

 412 Figure 4.12: Visualized examples in ACDC challenge. (left) Input image. (right) Ground truth [126].

challenge 5

 5 aims first to classify whether the patient is healthy or pathological from the attached clinical characteristics (e.g., age and ECG), available on a text file, with or without LGE-MRI, and then automatically segment the several relevant regions (the LV MYO, MI, and MVO). All acquisitions were performed with a 1.5 T and a 3T (Siemens Medical Solution, Erlangen, Germany) 10 minutes after injecting a contrast agent (Gd-DTPA; Magnevist Schering-AG, Berlin, Germany). The data include a stack of SA view slices with a slice thickness of 8 mm and pixel spacing between 1.25 × 1.25 mm 2 and 2 × 2 mm 2 .

Figure 4 .Figure 4 . 13 :

 4413 Figure 4.13: Visualized examples in MS-CMRSeg challenge. Rows from top to bottom represent the basal, middle and apical slices, respectively. (left) Input image. (right) Ground truth [180].

Figure 4 . 14 :

 414 Figure 4.14: Visualized examples in MyoPS challenge. From Left to Right: Input image, Ground truth, and 3D rendering [216].

Figure 5 . 1 :

 51 Figure 5.1: Two LGE-MRI slices of a patient, showing the original image (a, b) and the preprocessed image (c, d).

2 .

 2 In total the proposed model has 29 convolutional layers, 3 × 3 filter kernel of convolution, and an increased number of filters from 64 to 512 for extracting different feature representations. Our proposed methodology has a U-shaped encoder-decoder architecture. Max pooling indices[START_REF] Badrinarayanan | A deep convolutional encoder-decoder architecture for image segmentation[END_REF] and skip connections[START_REF] Ronneberger | Convolutional networks for biomedical image segmentation[END_REF] are incorporated to improve myocardial segmentation. The yellow arrows indicate the pooling indices and skip connections between the contracting part and the expansive part. Indeed, the proposed algorithm benefits from U-Net and SegNet based architectures, i.e., the capacity to capture fine image details and the computational efficacy. The model output for each input of sub 2.5D was one segmentation map with a size of 224 × 224 × 3, representing the predicted category of each pixel relative to the tissue type (MYO, scar, MVO, or background (BG) areas). The loss function was weighted to alleviate the class imbalance issue. The process is repeated for 100 epochs, using the following parameters: Batch size = 2, Adadelta[START_REF] Zeiler | Adadelta: an adaptive learning rate method[END_REF] as the optimizer algorithm, BN, and ReLU activation function were done on each layer of the model structure, and a multiclass softmax classifier with four labeled outputs: BG (0), MYO (1), MI (2), and MVO (3) as a model prediction.

Figure 5 . 2 :

 52 Figure 5.2: Architecture of the proposed 2.5D SegU-Net network based on latecombination method for myocardial segmentation.

5. 3 . 2 /

 32 ALGORITHM 2: SPIU-NET Algorithm 2: SPIU-Net Khawla Brahim, Abdul Qayyum, Alain Lalande, Arnaud Boucher, Anis Sakly, and Fabrice Meriaudeau. A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI IRBM, Elsevier, Date of Publication: February 15, 2021. DOI: 10.1016/j.irbm.2021.02.005Our second algorithm, SPIU-Net, improves the U-Net architecture by extending a shape prior constraint to help handle anatomical inconsistencies. We also incorporated several blocks (i.e., CBAM (Convolutional Block Attention Module), inception residual, and EDP (Expansion, Depth-wise, and Projection layers) blocks to the proposed algorithm).

Figure 5 .Figure 5 . 3 :

 553 Figure 5.3 shows our proposed shape prior-based model for fully myocardial segmentation. Segmenting the MYO is a significant stage towards identifying both healthy MYO and MI. The SPIU-Net model comprises two essential steps. We first introduce the anatomical network to segment the MYO and LV cavity structures. Then, the 3D pre-trained Autoencoder network and the 3D U-Net architecture were fused to produce the final myocardial segmentation. Details of each stage are explained in the following paragraphs.

  .

Figure 5 . 4 :

 54 Figure 5.4: Overview of the proposed anatomical network based on inception residual, CBAM, and decoder (EDP) blocks for accurate myocardium segmentation.

Figure 5 .

 5 Figure 5.5 shows the detailed architecture setting of the inception residual block. Compared with the original inception residual module, BN layer has been used after each convolutional layer, excluding bottleneck layers. We used 1 × 1 and 3 × 3 kernel, and also introduced 5 × 5 kernel branch as inspired by the DeepLab network [129]. BN layer makes smooth training and may avoid gradient vanishing while keeping convolutional layers. At each inception residual block we doubled the number of feature channels. The feature maps are aggregated through convolving with three kernels (i.e., 1 × 1, 3 × 3, and 5 × 5). The 3 × 3 and 5 × 5 kernels are further decreased into 1 × 3, 3 × 1, 1 × 5, and 5 × 1 to minimize the number of training parameters. Given x l is the output of the l th layer, c (n×n) (.) is a n × n kernel convolutional layer, c b (.)

Figure 5 . 5 :Figure 5 . 6 :Figure 5 . 7 :

 555657 Figure 5.5: Proposed inception residual block.

1

 1 Kernel 96 × 96 × 1 48 × 48 × 64 24 × 24 × 128 12 × 12 × 256 Shared 3 × 3 Kernel

Figure 5 . 8 :

 58 Figure 5.8: Proposed KASPP module.

Figure 5 . 9 :

 59 Figure5.9: Overall network structure of our 3D pathological approach for myocardial disease segmentation. The number of channels is denoted above every feature map.

Algorithm 3 :

 3 ICPIU-Net Khawla Brahim, Tewodros Weldebirhan Arega, Arnaud Boucher, Stephanie Bricq, Anis Sakly, and Fabrice Meriaudeau. An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Priors Information U-Net (ICPIU-Net) Submitted to Computer Methods and Programs in Biomedicine, Elsevier, September 15, 2021. The pipeline of our last algorithm, ICPIU-Net, is displayed in Figure 5.10. Since myocardial diseases (scar and MVO) are ensured to be localized within the MYO, it makes sense to first segment the MYO and LV cavity to remove other hyper-enhanced and hypoenhanced regions of the LGE-MRI. A block diagram of our ICPIU-Net approach is presented in Figure 5.11. In the training phase, Anatomical-Net and Pathology-Net were trained separately on 100 LGE-MRI. 50

Figure 5 . 10 :λ 2 λ 1 ICPIUFigure 5 . 11 :

 51021511 Figure 5.10: Pipeline of our proposed ICPIU-Net network for automatic myocardial disease segmentation. The red, green, blue, and yellow colors represent the LV cavity, the MYO, MI, and MVO.

Figure 5 . 12 :

 512 Figure 5.12: Schematic representation of the proposed pathological segmentation network. Both inclusion and class constraints are used to better supervise diseased myocardial segmentation.

Figure 6 . 1 :

 61 Figure 6.1: Representative three slices from three test input cases and relative masks. From left to right : (a) Original LGE-MRI , (b) 2.5D Input images, (c) First intra manual segmentation, (d) Second intra manual segmentation, and (e) 2.5D SegU-Net generatedresult. (MYO (blue), MI (green), and MVO (red)).

(α = β = 1 . 0 )

 10 performing much better than the SegU-Net trained with the dice loss function corresponding to α = β = 0.5 and other combinations of F-scores (α + β = 1.0). We also show how adjusting the hyperparameters of IOU performs highly with imbalanced data.f = T P + smooth T P + α × FN + β × FP + smooth(6.1)where smooth = 1e-5 and TP, FP, FN: were the abbreviations of True Positive, FalsePositive, and False Negative, respectively.

Figure 6 . 2 :

 62 Figure 6.2: A conductive report showing for each test patient the total number of slices presenting scar (green) and MVO (orange) regions compared to healthy MYO (blue). For each ratio, the numerator and the denominator represent the total number of slices with MI or MVO per case predicted by our proposed algorithm and manually segmented by an expert, respectively.

Figure 6 .

 6 Figure 6.3 and Figure 6.4 provide a comparison of the automated method to manual

Figure 6 . 3 :

 63 Figure 6.3: Boxplots of the evaluation of DSC for the final SegU-Net algorithm.

Figure 6 . 4 :

 64 Figure 6.4: Boxplots of the evaluation of IOU for the final SegU-Net algorithm.

(

  b) HD (mm) for the diverse segmentation methods.

Figure 6 . 5 :

 65 Figure 6.5: Comparison of the networks results. Our approach outperformed the 3D U-Net architecture in segmenting myocardial areas.

Figure 6 .

 6 Figure 6.7 shows examples of expert myocardial delineation and corresponding segmentation results of 3D U-Net and the final proposed model in our own split testing set. Our

(

  b) AVDR (%) of MI and MVO in relation to the full myocardial volume.

Figure 6 . 6 :

 66 Figure 6.6: Comparison of segmentation performance. The proposed SPIU-Net model achieves superior AVD and AVDR values on the MI, and MVO areas.

Figure 6 . 7 :

 67 Figure 6.7: Visual exemplar segmentations for our SPIU-Net model and 3D U-Net applied for the same subject on our own split testing set. Each row displays a patient on two heart locations, followed by a 3D surface rendering. The rows from top to bottom are the expert delineation, our approach prediction, and 3D U-Net segmentation. LV cavity, MYO, scar, and MVO are labeled in red, green, blue, and yellow, respectively.

Figure 6 .

 6 Figure 6.8 illustrates the MYO, scar, and MVO segmentation results of our proposed ICPIU-Net and[START_REF] Brahim | A 3d network based shape prior for automatic myocardial disease segmentation in delayed-enhancement mri[END_REF] for three diverse slices, randomly selected from three different subjects of the testing dataset. Segmented 2D slices were stacked up to reconstruct a 3D rendering surface of myocardial tissues. Compared to[START_REF] Brahim | A 3d network based shape prior for automatic myocardial disease segmentation in delayed-enhancement mri[END_REF], the ICPIU-Net model segmented infarct and MVO more accurately. Qualitative results show that the final segmen-

Figure 6 . 8 :

 68 Figure 6.8: Qualitative segmentation results. In the first fourth columns, input LGE-MRI, manually segmented contours, and examples of the segmentation results on three various slices extracted from LGE-MRI of three testing subjects produced by [211] network and the proposed ICPIU-Net model are displayed. The fifth column illustrates the 3D view of the myocardial tissues of our proposed method prediction. Red: LV cavity, Green: MYO, Blue: Scar, and Yellow: MVO.

Figure 6 .

 6 Figure 6.9 shows examples of the segmentation results of proposed model and expert delineations at basal, mid-ventricular, and apical slices. With the incorporation of the additional inclusion and classification constraints, the segmentation results reached a good consensus with the ground truths. Qualitative evaluations prove that our approach produced more robust segmentation results, especially at the middle slices. It can be observed that most segmentation errors appear at basal and apical slices.

Figure 6 . 10 :

 610 Figure 6.10: The graph shows the difference between the resulting network and ground truth volumes according to their average. (a), (b), and (c): Representative Bland-Altman plots of MYO, MI, and MVO volumes obtained from our ICPIU-Net model over testing examples, respectively.

7 CONCLUSION

 7 AND FUTURE WORK"As a technologist, I see how AI and the fourth industrial revolution will impact every aspect of people's lives" -Fei-Fei Li 7.1/ GENERAL CONCLUSION Myocardial scar segmentation of CMR is a highly challenging area of research that can afford critical information to aid diagnosis and therapy planning. As the manual segmentation for LV infarct is a time-consuming and observer-biased task, automatic myocardial segmentation using DL techniques has been extensively explored in the last decades to grow the survival rate.

  Studies have proved the feasibility of LGE-MRI to evaluate the viability of the myocardial segment. Promising results were acquired through a gadolinium-based contrast agent, putting into evidence re-perfused areas within the MYO wall as a sign of MI.In this context, the present dissertation has been realized to develop improved, robust, and fully automated DL-based segmentation networks to assess and quantify pathological tissues within the LV (MI, MVO), including healthy MYO on a set of contrast-enhanced acquisitions. To support this overall purpose, we evaluate their performances on EMIDEC datasets, comprising 100 training images and 50 test images from healthy and infarcted patients, to help cardiologists establish diagnosis and treatment pipelines faster. The methods' assessment and validation were achieved based on different evaluation metrics, including DSC, HD, and AVD. We demonstrated that the segmentation results were consistent with the manual contouring and that our proposed models compared favorably with methods recently developed for the EMIDEC challenge. Extensive experiments on CHAPTER 7. CONCLUSION AND FUTURE WORK two other public datasets (MS-CMRSeg, MyoPS) show that our resulting architectures achieved a good performance.The proposed process first used a pre-processing step on the complete LGE data. Next, the processed LGE-MR images are passed through proposed algorithms to segment normal MYO, infarct, and MVO regions. The framework results are finally post-processed to improve the segmentation accuracy. In the following, we summarize the principal contributions of this thesis.• We first proposed a 2.5D SegU-Net network based on fusing two DL architectures (U-Net and Seg-Net) for automated LGE-MRI myocardial disease segmentation in a new multifield expert annotated dataset (EMIDEC before being publicly available).

  cavity and the MYO, to produce a preliminary segmentation. Then, we proposed to combine 3D U-Net architecture and 3D Autoencoder segmentation network to incorporate shape prior information on the framework pipeline to ensure plausible segmentation of myocardial tissues. Experiments results have demonstrated the algorithm's performance in identifying myocardial areas.• Finally, an ICPIU-Net segmentation algorithm based on the inclusion and classification priors information was implemented to efficiently segment the normal MYO, scar, and MVO tissues. The proposed method first used nnU-Net architecture to segment the LV cavity and MYO. Then, our approach introduced the inclusion and classification information of the LGE-MRI to improve the resulting segmentation of the diseased tissues within the pre-segmented MYO. Compared to several DL-based models participating in the EMIDEC challenge for MI segmentation, our approach achieved a more significant agreement with the gold standard in segmenting myocardial diseases.7.2/ FUTURE PERSPECTIVESOur research on myocardial segmentation in LGE-MRI using prior constrained information opens several perspectives. The proposed networks are envisioned to foresee future research lines. It would be interesting to extend these algorithms to other modalities and heart conditions or to develop new improvements to the proposed algorithms. Our segmentation methods can be fused with traditional cardiac segmentation techniques to bring an excellent diagnostic package with high reliability. Further works are also needed to impose clinical information, intensity, and transverse sections (basal, middle, and apical) prior knowledge into the segmentation process. An uncertainty rectifying block can be introduced to boost the network performance, where the uncertainty estimation can be generated with a bayesian method. A memory module propagating the initial segmentation over the whole volume can also be incorporated to guide the next slice to segment based on the current segmentation of each slice. There is still much research work possible to improve MI segmentation. Despite the success of CNNs, the locality of convolution operations limits their ability to learn global context and long-range spatial dependencies. Recently, transformers, developed initially to solve natural language processing tasks, are considered the alternative methods to U-Nets. Nevertheless, they focus only on the global context but fail to extract detailed localization features. Transformers demonstrate outstanding performance in several DL tasks, including image recognition. Nowadays, few studies have used pure transformer and transformer-CNN (UNETR, vision transformer (ViT)) hybrid methods for medical image segmentation and classification. In the future, transformer-based architectures can be applied to EMIDEC dataset for improved segmentation of myocardial diseases.écol e doct oral e sci ences pour l 'i ngéni eur et mi crotechni ques Universit é Bourgogne Franche-Comt é 32, avenue de l'Observatoire 25000 Besanc ¸on, France Title: Deep learning architectures for automatic detection of viable myocardiac segments Keywords: Myocardial segmentation, LGE-MRI, Deep learning, Prior shape, Myocardium, Topological constraints, Pathological tissues.

  used a U-Net-based architecture that provides spatially consistent results on a whole volume through propagating the segmentation across slices to perform 2D segmentation on both ventricles, including the MYO. Their network accuracy appears to be in line with manual delineations, achieving a Percentage of Good Contours (PGC, defined in[START_REF] Radau | Evaluation framework for algorithms segmenting short axis cardiac mri[END_REF]) of "99.21%" for LV epicardial and "97.08%" for LV cavity with the Sun-

nybrook dataset (see section 4.4.1). Moeskops et al.

[START_REF] Moeskops | Deep learning for multi-task medical image segmentation in multiple modalities[END_REF]

proposed a 2.5D approach to assess whether a single CNN can perform several segmentation tasks. The authors proved this concept using multiple modalities (i.e., brain MRI, breast MRI, and cardiac CT angiography) for each of the three segmentation tasks. Their results showed that such a system could visualize diverse anatomical structures with multiple modalities without task-specific training. Other 2.5D approaches proposed hybrid image segmentation using a DL network fused with statistical shape modeling. For instance, Wang and Smedby

[START_REF] Wang | Automatic whole heart segmentation using deep learning and shape context[END_REF]

correlated orthogonal 2D U-Nets' outputs with a volumetric shape prior for better delineations. Their network demonstrated great results on CMRI segmentation.

Table 4 .

 4 1: Segmentation accuracy of previous works on the testing (ACDC) dataset. Best dice value for each structure is shown in bold. techniques in terms of speed and accuracy. A variety of more advanced FCNs-based studies have been developed to perform remarkable improvements in segmentation performance. Indeed, much research has been conducted on optimiz-

	Methods	LV	End diastolic RV MYO	LV	End systolic RV	MYO
	Isensee et al. [97]	0.968	0.946	0.902	0.931	0.899	0.919
	Baumgartner et al. [91]	0.963	0.932	0.892	0.911	0.883	0.901
	Jang et al. [98]	0.959	0.929	0.875	0.921	0.885	0.895
	Zotti et al. [125]	0.957	0.941	0.884	0.905	0.882	0.896
	Khened et al. [169]	0.964	0.935	0.889	0.917	0.879	0.898
	Wolterink et al. [115]	0.961	0.928	0.875	0.918	0.872	0.894
	Patravali et al. [108]	0.955	0.911	0.882	0.885	0.819	0.897
	Roh é et al. [110]	0.957	0.916	0.867	0.900	0.845	0.869
	Tziritas and Grinias [93]	0.948	0.863	0.794	0.865	0.743	0.801
	Yang et al. [117]	0.864	0.789	N/A	0.775	0.770	N/A
	Li et al. [172]	0.967	0.920	0.949	0.902	0.905	0.917
	Zotti et al. [156]	0.963	0.934	0.886	0.912	0.885	0.902
	Painchaud et al. [178]	0.961	0.933	0.881	0.911	0.884	0.897

*Clinical metrics were also taken into account for the ranking.

4.3.1.1/ FCN-BASED SEGMENTATION

Tran

[START_REF] Tran | A fully convolutional neural network for cardiac segmentation in short-axis mri[END_REF]

uses an FCN

[START_REF] Shi | An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition[END_REF]

to segment the LV, MYO, and RV on SA CMRI. Their automated FCN-based method reached significant segmentation performance compared to classical loss) for better segmentation performance. Due to CMR scans' motion artifacts, restricting the applicability of 3D approaches for segmentation

[START_REF] Baumgartner | An exploration of 2d and 3d deep learning techniques for cardiac mr image seg-mentation[END_REF]

, most FCN-based networks utilize 2D architectures, which cope better in these cases.

4.3.1.2/ TEMPORAL AND SPATIAL COHERENCE

As 2D Networks are used to process every 2D slice of a whole volume, instead of a 3D

  .3.2/ SCAR SEGMENTATION 4.3.2.1/ SCARCITY OF NETWORKS DUE TO A MORE RECENT INTEREST

	[178] developed a Variational AE (VAE) to refine the network's output through correcting
	non-anatomically plausible segmentation masks in the post-processing step.
	4.3.1.4/ MULTI-TASK LEARNING
	Over the years, there is an increased interest in regularizing cardiac segmentation net-
	works through achieving simultaneously additional tasks that are pertinent to the principal
	segmentation task (e.g., ventricle size classification [152] and cardiac function estimation
	[131]). Training a model for several tasks helps the model learn valuable features for
	better segmentation accuracy.
	segmented.
	4.3.1.6/ HYBRID SEGMENTATION APPROACHES
	Some approaches focus on fusion DL methods with traditional cardiac segmentation ones
	such deformable models [68, 176], graph-cut segmentation algorithms [175], and level-
	sets methods [106, 132] for better generalization. DL models are used for informative
	feature extraction and initialization steps to minimize manual interactions dependence
	and reach higher segmentation performance. For instance, Avendi et al. [68] adopted

Zotti et al.

[START_REF] Zotti | Convolutional neural network with shape prior applied to cardiac mri segmentation[END_REF]

developed a GridNet-based network that incorporates a cardiac shape prior to help kinetic cardiac MRI segmentation. Unlike these models, Painchaud et al.

4.3.1.5/ MULTI-STAGE MODELS

Various automated networks have been designed using a multi-stage pipeline, dividing the main segmentation task into subtasks

[START_REF] Vigneault | ωnet (omega-net): fully automatic, multi-view cardiac mr detection, orientation, and segmentation with deep neural networks[END_REF][START_REF] Zheng | 3-d consistent and robust segmentation of cardiac images by deep learning with spatial propagation[END_REF][START_REF] Li | Apcpnet: aggregated parallel cross-scale pyramid network for cmr segmentation[END_REF][START_REF] Liao | Estimation of the volume of the left ventricle from mri images using deep neural networks[END_REF][START_REF] Tan | Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine mr sequences[END_REF][START_REF] Vesal | Fully automated 3d cardiac mri localisation and segmentation using deep neural networks[END_REF][START_REF] Wu | Automated left ventricular segmentation from cardiac magnetic resonance images via adversarial learning with multistage pose estimation network and co-discriminator[END_REF]

]. An essential stage in the cardiac segmentation method is automatically localizing the target structure in the MRI volume to segment slices based on the localization result, reducing computational complexity. For example, Sulaiman et al.

[START_REF] Vesal | Fully automated 3d cardiac mri localisation and segmentation using deep neural networks[END_REF]

first estimate a coarse density map localizing the structure of interest to then focus the second stage of the model (segmentation) in the Region(s) Of Interest (ROI). Their network achieves better segmentation performance than previous CNN-based segmentation methods. Morever, Omega-Net developed by Vigneault et al.

[START_REF] Vigneault | ωnet (omega-net): fully automatic, multi-view cardiac mr detection, orientation, and segmentation with deep neural networks[END_REF]

for the ACDC challenge, includes several stages: First, an initial segmentation is achieved on the input image. Subsequently, the features learned over this initial stage are employed to predict the parameters required for transforming the image into a canonical orientation. Finally, the transformed image is a CNN to detect the LV in cardiac SA images and an AE to deduce the LV shape. This deduced shape is integrated into deformable models to get the best compromise for a more accurate segmentation result. Alike, Ngo et al.

[START_REF] Ngo | Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance[END_REF]

applied a Deep Belief Network (DBN) to direct a level-set method for accurately LV segmentation.

4

Table 4 .

 4 3 depicts the MRI cardiac dataset details mentioned below.

Table 4 .

 4 3: Summary of benchmarking CMRI datasets available for comparison goals.

	Name	Year	Nb subjects			Ground truth
			Train	Test	LV	RV	MYO	Pathology
	Sunnybrook	2009	45	-
	LV	2011	100	100
	LivScar	2012	10	20
	ACDC	2017	100	50
	EMIDEC	2020	150	50
	4.4.1/ SUNNYBROOK CMR LV SEGMENTATION CHALLENGE -MICCAI 2009

Table 6 .

 6 1: Metrics comparing intra-observer manual myocardial correlations.

	Local measures	BG	MYO	MI	MVO
	IOU	0.99665	0.67385	0.50625	0.57868
	Accuracy	0.99842	0.77974	0.74108	0.92347
	DSC	0.99836	0.80834	0.67561	0.71602
	6.2.1.2/ EVALUATION RESULTS

OF SEGU-NET METHODOLOGY

In this study, a 2.5D SegU-Net has been trained to perform accurate automatic segmentation of infarcted subjects. Qualitative and quantitative evaluations were performed.

Table 6 .

 6 2 and Table6.3 demonstrate the agreement between the proposed automated segmentation and intra-observer manual masks in identifying myocardial contouring using IOU, Accuracy, and DSC comparison metrics. These performance metrics have been measured on base, middle, and apex slices. For considered metrics, our proposed network achieved better scores against the intra-observer study. The extensive validation of our algorithm turns this proposal into an efficient tool with clinical transfer potential.

	Table 6.2: Results. Final SegU-Net values % First intra-observer manual segmentation.
	Local measures	BG	MYO	MI	MVO
	IOU	0.99998	0.98960	0.91808	0.58423
	Accuracy	0.99999	0.99805	0.96945	0.90470
	DSC	0.99999	0.99478	0.95729	0.73755

Table 6 . 3 :

 63 Results. Final SegU-Net values % Second intra-observer manual segmentation. Net with several α and β values were compared with the expert annotations by using precision, recall, F1-score, and IOU metrics. These values settle the degree of penalties for FNs and FPs, respectively. Table6.6 reports the performance metrics (on the test data sets). For all considered test measures, the best results were achieved using the SegU-Net model trained with IOU loss function Table 6.4: Results. SegU-Net values % Second intra-expert delineation (Loss Function = Categorical focal-jaccard Loss).

	Local measures	BG	MYO	MI	MVO
	IOU	0.99998	0.98874	0.91547	0.64371
	Accuracy	0.99999	0.99873	0.96273	0.91972
	DSC	0.99999	0.99434	0.95587	0.78324
	Table 6.4 and Table 6.5 illustrate that SegU-Net network with focal-jaccard loss function
	combination achieved the best trade-off between considered evaluation metrics agreeing
	with manually segmented test data. Promising results performed through adding the
	Focal loss[102] function show the relevance of SegU-Net model in detecting diseased
	myocardial areas.
	To analyse the impact of the loss function f summarized in Eq. 6.1, segmentation results
	obtained from training our SegU-

Table 6 .

 6 6: Comparative study of the MYO on the test set conducted for several values of the hyperparameters α and β used in training the SegU-Net.

	Average test	Precision	Recall	F1 score	IOU
	α = β = 1.0	0.99987	0.99979	0.99982	0.95607
	α = β = 0.5	0.99969	0.99955	0.99959	0.92662
	α = 0.4 , β = 0.6	0.99935	0.99917	0.99921	0.94996
	α = 0.3 , β = 0.7	0.98725	0.98806	0.98657	0.60654
	α = 0.7 , β = 0.3	0.99124	0.99444	0.99252	0.89903
	α = 0.6 , β = 0.4	0.98967	0.98868	0.98719	0.63285
	Our algorithm predicted 52 out of 53 LGE-MRI slices presenting MVO areas when com-

Table 6 .

 6 7: Quantitative evaluation for myocardial segmentation methods.

	Methods	Metrics	BG	Structures MYO MI	MVO
		IOU	0.99998 0.98874 0.91547 0.64371
	SegU-Net	Accuracy 0.99999 0.99873 0.96273 0.91972
		DSC	0.99999 0.99434 0.95587 0.78324
		IOU	0.99984 0.95443 0.73963 0.31258
	[96]	Accuracy 0.99989 0.99467 0.85563 0.82690
		DSC	0.99992 0.97668 0.85033 0.47628
		IOU	0.99737 0.74302 0.19792 0.00459
	[64]	Accuracy 0.99861 0.90564 0.41232 0.74867
		DSC	0.99868 0.85257 0.33044 0.00913
		IOU	0.99257 0.80819 0.20069	0.0000
	[135]	Accuracy 0.99262 0.92750 0.91291 0.74762
		DSC	0.99627 0.89392 0.33429	0.0000
		IOU	0.99761 0.82020 0.11265	0.0000
	[157]	Accuracy 0.99963 0.91314 0.33900 0.74762
		DSC	0.99881 0.90122 0.20249	0.0000
		IOU	0.99980 0.80202	0.0000	0.0000
	[72]	Accuracy 0.99996 0.99254 0.21429 0.74762
		DSC	0.99990 0.89014	0.0000	0.0000
		IOU	0.99956 0.77503	0.0000	0.0000
	[81]	Accuracy 0.99993 0.96345 0.21429 0.74762
		DSC	0.99978 0.87326	0.0000	0.0000
		IOU	0.99030 0.27225	0.0000	0.0000
	[153]	Accuracy 0.99824 0.31130 0.21905 0.77619
		DSC	0.99513 0.42799	0.0000	0.0000
	Table 6.8: Results. SegU-Net values on MS-CMRSeg dataset.
	Local measures	MYO	LV	RV
		IOU	0.75360	0.78380	0.94241
		Accuracy	0.80848	0.95228	0.93370
		DSC	0.85949	0.87880	0.97035
	6.2.2/ EXPERIMENT TWO -SPIU-NET SEGMENTATION
	6.2.2.1/ SEGMENTATION PERFORMANCE OF SPIU-NET METHODOLOGY ON EMIDEC

Table 6 .

 6 9: Quantitative myocardial segmentation performance in 5-fold cross-validation.

	Targets	Metrics	fold0	fold1	5-fold cross-validation fold2 fold3 fold4	Avg	Std
		DSC (%)	95.27	94.40	95.11	95.23	95.49	95.10	0.37
	MYO	AVD (mm 3 ) 256.81 343.50 247.88 252.19 231.69 266.41 39.46
		HD (mm)	4.41	5.29	4.36	4.06	3.89	4.40	0.48
		DSC (%)	74.93	77.85	76.37	76.52	75.02	76.14	1.08
	MI	AVD (mm 3 ) 342.00 308.69 212.31 176.75 284.81 264.91 61.31
		AVDR (%)	6.82	6.87	4.09	2.99	5.84	5.32	1.54
		DSC (%)	71.60	77.03	77.64	70.60	72.06	73.79	2.94
	MVO	AVD (mm 3 )	41.06	34.50	60.00	76.31	63.56	55.09	15.28
		AVDR (%)	0.75	0.75	1.10	1.41	1.24	1.05	0.26
	Summary of the quantitative results tabulated in Table 6.10 proves that our segmenta-
	tion results achieved a better consensus with the manual references than the results of
	the competing networks. It can be observed that myocardial regions were consistently
	segmented with high DSC and HD.

Table 6 .

 6 10: Performance comparison of various segmentation networks using 5-fold cross-validation. Bold result values are the best.We conducted extensive experiments for a comparative study of different methods based on wIOU and mwIOU loss functions. The SPIU-Net algorithm is also compared with the baseline 3D U-Net architecture to demonstrate the significance of enforcing shape prior information in the final prediction. Figure6.5 and Figure6.6 illustrate plots of DSC, HD,

	Targets	Metrics	[201]	Methods [209]	SPIU-NET
		DSC (%)	81.00	94.40	95.10
	MYO	AVD (mm 3 ) 13655.55 6474.38	266.41
		HD (mm)	16.72	17.21	4.40
		DSC (%)	36.08	72.08	76.14
	MI	AVD (mm 3 )	8980.5	4179.5	264.91
		AVDR (%)	7.07	3.41	5.32
		DSC (%)	54.15	71.01	73.79
	MVO	AVD (mm 3 )	1501.73	918.69	55.09
		AVDR (%)	1.08	0.69	1.05

  AVD (mm 3 ) between methods and the expert target.

													μ = Median AVD
													3D Unet + wIOU
													3D Unet + mwIOU
													Our approach + wIOU
													Our approach + mwIOU
			μ=255.50	μ=276.50	μ=207.00	μ=240.50	μ=548.50	μ=343.50	μ=280.00	μ=151.00	μ=107.00	μ=35.00	μ=29.50	μ=17.50
	VolumeDifferenceRatio (AVDR)	-0.05 0.35 0.00 0.05 0.10 0.15 0.20 0.25 0.30	μ=0.11	(a) Infarction μ=0.07 μ=0.06	μ=0.04		μ=0.02	μ=0.01	MVO	μ=0.01 μ = Median AVDR μ=0.00 3D Unet + wIOU 3D Unet + mwIOU Our approach + wIOU Our approach + mwIOU
								Structure

Table 6 .

 6 [START_REF] Simonetti | An improved mr imaging technique for the visualization of myocardial infarction[END_REF] demonstrates how the incorporation of the pathological network, which is re-

sponsible for detecting the diseased tissues, affects the segmentation performance of the MYO and LV cavity structures produced by the anatomical network. The table quantitatively shows the anatomical segmentation network's performances alone and after integrating the shape prior auxiliary model into it.

Table 6 .

 6 11: Performance analysis and comparison between the proposed anatomical model and our final SPIU-NET network (anatomical + pathological) without and with using post-processing. An example is presented for one fold as 5 fold-cross validation is done. timized parameters setting chosen during training with the EMIDEC dataset. The data has been preprocessed based on the multivariate mixture method (MvMM)[START_REF] Zhuang | Multivariate mixture model for myocardial segmentation combining multi-source images[END_REF]. Table6.12 and Table6.13 illustrate the performance comparison of the proposed anatomical with state-of-the-art networks on MS-CMRSeg and MyoPS challenges, respectively. These tables show that our anatomical approach achieved excellent DSC scores as compared to previous methods. The experiments prove the efficiency of our proposed anatomical network on other clinical datasets.

	Targets	Metrics	Methods Anatomical model Without post-processing SPIU-NET
	MYO	DSC (%) AVD (mm 3 )	91.18 348.62	95.27 256.81	95.07 315.5
		HD (mm)	23.65	4.41	5.02
	Infarction	DSC (%) AVD (mm 3 )	--	74.93 342.00	76.56 234.12
		AVDR (%)	-	6.82	4.92
	MVO	DSC (%) AVD (mm 3 )	--	71.60 41.06	83.77 30.25
		AVDR (%)	-	0.75	0.60
	6.2.2.2/ VALIDATION OF THE PERFORMANCE OF THE PROPOSED ANATOMICAL NET-

WORK ON BENCHMARK DATASETS

To further evaluate the performance of the proposed anatomical model, we tested the proposed anatomical model on other publicly available LGE-MRI datasets using the same op-

Table 6 .

 6 12: Performance analysis and comparison of myocardial results of the proposed anatomical network and existing DL-based networks on MS-CMRSeg database. (DSC (%) and HD (mm)). Best results are highlighted in bold font.

	Methods	Dataset	DSC HD
	Proposed Anatomical model	BSSFP+ T1 for training LGE	81.87 9.98
	[184]	Used different combination for training LGE 74.90 11.35
	[158]	Used different combination for training LGE 68.00 12.00

Table 6 .

 6 13: Performance analysis and comparison of myocardial results of the proposed anatomical network and existing DL-based networks on MyoPS database. Best results are highlighted in bold font.

	Methods	DSC (%) HD (mm) AVD (ml)
	Proposed anatomical model	84.69	16.3190	8.6275
	[200]	82.17	17.9299	7.3824
	[179]	80.05	23.2728	11.6204
	[64]	0.7956 114.7892 26.3757
	6.2.3/ EXPERIMENT THREE -ICPIU-NET SEGMENTATION

Table 6 .

 6 14: Comparative study for EMIDEC segmentation results[START_REF] Lalande | Deep learning methods for automatic evaluation of delayed enhancement-mri. the results of the emidec challenge[END_REF]. (DSC (%), HD (mm), AVD (mm 3 ), and AVDR (%)).Significant results are marked in bold font. .04 14.[START_REF] Cigla | Region-based image segmentation via graph cuts[END_REF] 58.30 3380.43 2.57 74.97 626.33 0.38 [70] 87.77 9454.43 13.07 70.76 3214.65 2.48 78.90 540.95 0.34 ICPIU-NET 87.65 8863.41 13.10 73.

					Structures
	Methods		MYO		MI		MVO
		DSC	AVD	HD DSC	AVD	AVDR DSC	AVD AVDR
	[198]	83.56 15187.48 33.77 54.68 3970.73 2.89 72.22 883.42 0.53
	[201]	84.08 10874.47 18.3 37.87 6166.01 4.93 52.25 953.47 0.64
	[208]	85.53 16539.52 13.23 62.79 5343.69 4.37 60.99 1851.52 1.69
	[209]	87.86 9258.24 13.01 71.24 3117.88 2.38 78.51 634.69 0.38
	[193]	75.74 17108.13 25.44 30.79 4868.56 3.64 60.52 867.86 0.52
	[210]	82.46 13292.68 83.42 37.77 6104.99 4.71 51.98 879.99 0.54
	[199]	80.26 11807.68 51.48 34.00 11521.71 8.58 78.00 891.13 0.51
	[64]	85.82 11368

36 2693.84 1.95 81.31 511.25 0.32Table 6 .

 6 15: Performance metrics for various values of training hyperparameters λ IC and λ CC . (DSC (%), HD (mm), AVD (mm 3 ), and AVDR (%)). Significant results are marked in bold font. IC = λ CC = 0 87.77 9381.77 13.07 65.05 3096.54 2.39 78.82 553.56 0.34 (λ IC 0) ∧ (λ CC = 0) 87.74 9201.04 13.09 71.71 2830.32 2.15 80.99 538.60 0.34

				Structures
	Hyperparameters	MYO		MI	MVO
	DSC	AVD	HD DSC	AVD AVDR DSC AVD AVDR
	λ

ICPIU-NET 87.65 8863.41 13.10 73.36 2693.84 1.95 81.31 511.25 0.32 umes

  metrics. Nevertheless, the accuracy highlights the efficiency of our approach to segmenting MVO tissues. Table6.[START_REF] Warfield | Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation[END_REF] shows the additional metrics of MVO regions. Segmentation results demonstrate the effectiveness of inclusion and classification constraints in identifying MVO areas.

Table 6 .

 6 [START_REF] Warfield | Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation[END_REF]: Supplementary metrics for myocardial segmentation[START_REF] Lalande | Deep learning methods for automatic evaluation of delayed enhancement-mri. the results of the emidec challenge[END_REF]. Best results are highlighted in bold font.MVOAccuracy (case,%) 80.00 70.00 76.00 84.00 74.00 64.00 78.00 84.00 Accuracy (slice,%) 90.78 85.75 81.56 94.97 84.36 86.87 89.66 94.97 6.3/ CONCLUSION This chapter compares the segmentation results of three proposed networks, presented in chapter 5, against expert annotated delineation and some other methods on the 50 test sets. Experimental results on the EMIDEC dataset demonstrate that the proposed ICPIU-Net outperforms the state-of-the-art myocardial segmentation networks.

	Metrics	Methods [198] [201] [208] [209] [193] [210] [199] ICPIU-NET

Source:https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

Source:http://simple.wikipedia.org/wiki/Myocardial infarction

Source:http://mriquestions.com/ps-phase-sensitive-ir.html

Source:https://www.escardio.org/Education/Digital-Health-and-Cardiology/Virtual-Journal/ how-to-read-this-cardiac-segmentation-with-ai

Source:https://github.com/EMIDEC-Challenge/Evaluation-metrics/.

Source:https://www.creatis.insa-lyon.fr/Challenge/acdc/

Source:http://smial.sri.utoronto.ca/LVChallenge/Home.htmls/

Source:www.cardiacatlas.org/challenges/lv-segmentation-challenge/

Source:https://www.doc.ic.ac.uk/ ∼ rkarim/la lv framework/lv infarct/index.html/

Source:http://emidec.com//

Source:https://zmiclab.github.io/projects/mscmrseg19/

Source:http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/MyoPS20/

ACKNOWLEDGEMENTS

"No man is an island entire of itself; every man is a piece of the continent, a part of the main."

False True True True False True True True True True (FP) False False False True False True False False False True False True False True True False True False False True False True False False True Predicted as MI False True True True True True True True True True True True True True True True True True False True True True True True True True True True True True True True True True True