LIST OF FIGURES

Figure 16 -Intensity-based registration framework with its main components. Preoperative arterial phase Computed Tomography (CTA) images are semi-automatically segmented using endosize®, obtaining either a 3D bony-structure or delimited vascular-structure (1). DRR image is generated from the preprocessed volume (2) and compared to the 2D intraoperative image through a similarity metric presenting a quantitative criterion describing how well the input images are similar [START_REF] Sutherland | Claude Couinaud: A Passion for the Liver[END_REF]. For optimization, the 3D volume is intermediately transformed following an exhaustive search and the transformation result is progressively refined following a multiresolution scheme [START_REF] Brunicardi | Schwartz's principles of surgery[END_REF]. The final 3D/2D transformation corresponds to the transformation giving the best achieved similarity measure [START_REF]Overview of Blood Vessel Disorders of the Liver -Liver and Gallbladder Disorders[END_REF] is the world coordinate system, is the intraoperative image coordinate system, is the preoperative volume coordinate system and is the coordinate system where:

LIST OF TABLES

HVSS

HVS along with the splenic artery

GI

Gradient Information

GD

Gradient Difference

GC

Gradient Correlation

NCC

Normalized Cross Correlation

MI

Mutual Information

GS

Gold Standard

IoU

Intersection Over Union

IR

Inclusion Rate

Résumé long en français

La radioembolisation est une intervention mini-invasive réalisée pour traiter le cancer du foie.

Cette thérapie vise à détruire sélectivement les cellules cancéreuses en administrant des microsphères radioactives incorporant soit de l'Yttrium-90 (90Y) soit du Holmium-166 Même si le recalage osseux (Figure 2

(166Ho

Introduction

Minimally invasive interventions include the techniques that limit the size of the incisions and thus reduce the pain, the wound healing time, the associated infection risk [1] Due to the patient's respiration, the liver changes its position continuously. In this context, we also intend to extend the strategy to a dynamic update of the spatial alignment of the preoperative hepatic vasculature with the intraoperative hepatic vasculature position.

In the first chapter of this thesis we will introduce the clinical context of radioembolization as well as the most prominent challenges that may encounter radiologists during the procedure.

In the second chapter, we address the issue of 3D/2D registration to correctly align and project vascular structures extracted from preoperative CTA onto 2D intraoperative X-Ray images. The proposed registration method is evaluated on clinical data.

The third chapter describes the employment of a 2D/2D image-based registration between pretreatment and treatment fluoroscopic images performed to reproduce the injection site located beforehand during the pretreatment assessment intervention.

The fourth chapter deals with the dynamic overlay of the projected 3D vascular structures on the intraoperative fluoroscopy to improve catheter navigation by integrating the liver motion induced by breathing. To this end, a CNN-based method is more particularly investigated to detect the catheter position in real-time.

Lastly, we summarize our contribution and present perspectives to improve the proposed computer-assisted navigation approach.

Computer-assisted radioembolization: clinical context & proposed approach

We present, in this chapter, an overview of the clinical context of radioembolization while describing the liver anatomy, the hepatic cancer development and characteristics, and the radioembolization procedure. Subsequently, we outline a description of radioembolization typical image-based intervention guidance while mentioning the main limits of using fluoroscopic images, to better understand how our guidance strategy could improve the visualization in such procedures. The complete proposed guiding strategy which constitutes the foundation of this thesis is being introduced and depicted at the end of this chapter.

Clinical context 1.Liver anatomy

The liver is the largest organ in the human body, accounting for approximately 2% to 3% of average body weight [2]. It is able to perform a wide range of complex and vital functions such as:

-Glycogen storage: excessive entered glucose to the body is stored in the liver as glycogen which can be converted back to glucose and released into the blood when needed (in case of glucose levels fall).

-Bile production: bile is greenish yellow fluid, formed in the liver to aid the small intestine in breaking down and absorbing cholesterol, fats and some vitamins. The bile can be stored in the gallbladder before being released into the small intestine. In this case, it becomes more concentrated, which increases its potency and intensifies its effect.

-Production of certain proteins: the liver produces hundreds of different proteins, each of which has a specific function: prealbumin carries thyroid hormones in the blood, albumin has many functions like providing nourishment of tissues, transferrin presents the principal transport protein for iron, retinol-binding protein is responsible for transporting vitamin A from the liver to other body tissues, etc.

-Blood detoxification and purification: the liver filters the blood by removing toxins and harmful substances like drugs, alcohol etc.

Clinical context 29 -Immunity: kupffer cells, presenting one of the most important types of liver cells, aid in removing bacteria, worn blood cells, parasites and fungi from the blood.

Anatomical position

The liver mainly covers the right upper quadrant of the abdomen cavity, within the rib cage.

More specifically, it lies in the right hypochondriac region, epigastric region and extends to the left hypochondriac region. To maintain its position, the liver is attached to the surrounding structures through peritoneal reflections called ligamentous attachments (Figure 5).

Ligaments of the liver

The falciform ligament attaches the anterior surface of the liver to the anterior abdominal wall. The coronary ligament connects the central superior surface of the liver to the interior surface of the diaphragm while delimiting the bare area. As shown in (Figure 5), the anterior and posterior layers of the coronary ligament join to form two triangular ligaments located on the lateral borders of the liver. These left and right triangular ligaments attach the upper surface of the left and right lobes of the liver, respectively, to the diaphragm. The liver is connected to the lesser curvature of the stomach and a part of the duodenum by hepatogastric ligament and hepatoduodenal ligament, respectively.

Liver surface

The liver's upper (diaphragmatic) surface is entirely related to the diaphragm and its visceral surface rests against the gallbladder, right kidney and suprarenal gland, stomach, duodenum and the hepatic flexure. The posterior liver surface is in touch with the inferior vena cava and the esophagus. Couinaud's classification [START_REF] Sutherland | Claude Couinaud: A Passion for the Liver[END_REF] divides the liver into eight independent functional segments, based on the fact that each segment has its own blood supply and bile duct, and a separate hepatic venous branch that provides outflow. As shown in (Figure 6), segments II, III and IV make up the functional left lobe, V, VI, VII and VIII make up the functional right lobe and the caudate lobe, located posteriorly, is made up by the segment I.

Quadrate lobe

Hepatic vasculature

The liver receives blood mainly from two sources: hepatic artery (20-40%) and portal vein (60-80%), except in the case of hepatic tumors where a higher proportion of liver blood is derived from hepatic artery (Figure 7). The hepatic artery supplies the liver with oxygen-rich blood (pumped from the heart) needed for hepatic cells survive while portal vein collects nutrients with blood from the digestive system including: intestine, stomach, gallbladder, pancreas and spleen. The blood is drained to the heart through the hepatic vein, leading to the inferior vena cava.

Arterial vasculature

Although the hepatic vasculature is variable, in the most common configuration, the common hepatic artery (CHA), along with the splenic artery (SA) and the left gastric, originate from the celiac trunk which is branched from the abdominal aorta. As illustrated in Figure 9, CHA proceeds laterally and divides into the gastroduodenal artery (GDA) and the proper hepatic artery (PHA). GDA proceeds to supply the pylorus of the stomach, the proximal duodenum and the pancreas. PHA branches, itself, to right hepatic artery (RHA) and left hepatic artery (LHA) to supply the corresponding liver lobes. RHA gives rise to an artery called "cystic artery" to supply the gallbladder.

Liver cancer & treatment

Due to different factors, the liver can be damaged in a variety of ways. Different diseases can disturb the liver functions; we can categorize them by their causes:

-Infection: viruses can infect the liver and cause inflammation. Among the most common types of liver infections are hepatitis A, B and C.

-Immune system abnormality: instead of attacking viruses and bacteria, the immune system might attack the liver (autoimmune), causing a chronic inflammation and serious hepatic cells damage. Autoimmune liver diseases include primary biliary cirrhosis, primary sclerosing cholangitis and autoimmune hepatitis.

-Genetics: some abnormal genes inherited from one or both parents can cause genetic liver diseases like Wilson's disease, Alpha-1 antitrypsin deficiency and hemochromatosis.

-Lifestyle factors: alcohol heavy use, obesity, diabetes and improper medication use can contribute to cirrhosis, nonalcoholic fatty liver disease, the infiltration and growth of cancerous or noncancerous cells and many other hepatic problems.

Cancer is the second-leading cause of death in the world, presents abnormal cells that grow and divide uncontrollably and has the ability to infiltrate and destroy healthy tissue. Cancer can develop in the liver, destroy its cells and interfere with its ability to function normally.

Besides that it can primarily be formed in the liver (primary cancer), hepatic cancer can begin in another organ (e.g. lung, colon or breast) and spreads to the liver (metastatic cancer). Each year, over 800.000 people are diagnosed with liver cancer throughout the world. It presents a leading cause of cancer deaths, with more than 700.000 deaths every year [START_REF]Key Statistics About Liver Cancer[END_REF].

The most known primary cancer type, which is considered as the sixth most common malignancy worldwide and represents about 80% of primary liver cancer cases, is called hepatocellular carcinoma (HCC) [START_REF] Stewart | [END_REF]. HCC is a hypervascular tumor which begins in hepatocyte, presenting the main liver cell type. Unlike normal liver vascularization, HCC is almost exclusively arterially vascularized. HCC often occurs in persons with chronic liver diseases, such as cirrhosis caused by hepatitis B or hepatitis C infection [START_REF] Singh | Hepatocellular Carcinoma: Causes, Mechanism of Progression and Biomarkers[END_REF] and nonalcoholic fatty liver disease mainly caused by diabetes or obesity [START_REF] Dhamija | Non-alcoholic fatty liver disease associated with hepatocellular carcinoma: An increasing concern[END_REF]. Some other factors may raise the risk of cancer development such as: alcohol heavy drinking and certain drugs use like anabolic steroids (body building steroids). Hepatic cancer diagnosis is generally performed mostly for vulnerable people for which the presence of other diseases that may have the same symptoms are ruled out. Biopsy, blood tests or imaging tests are generally recommended for diagnosis.

Liver cancer treatment options are determined by considering the cancer stage, describing the tumor widespread, and the patient's overall general health. The stage is generally determined following a specific staging system: e.g. Barcelona Clinic Liver Cancer (BCLC) classification system [START_REF] Llovet | Prognosis of hepatocellular carcinoma: the BCLC staging classification[END_REF], the tumor-node-metastasis (TNM) system [START_REF] Edge | The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM[END_REF], Okuda system [START_REF] Okuda | Prognosis of primary hepatocellular carcinoma[END_REF], or the Cancer of the Liver Italian Program (CLIP) system [START_REF]A new prognostic system for hepatocellular carcinoma: a retrospective study of 435 patients: the Cancer of the Liver Italian Program (CLIP) investigators[END_REF].

In general, all types of liver cancer are among the most difficult diseases to be treated, except the case of a primary cancer diagnosed in an early stage. In such case, the tumor can be removed by curative treatment (i.e. surgery) whose recovery chance is high. Curative treatment options include:

-Partial liver resection (hepatectomy): part of liver containing the tumor(s) is removed. The hepatectomy is recommended when the tumor(s) is located in one lobe of the liver. The remaining lobe takes over the functions of the entire liver.

The resected part of liver may grows back again within a few weeks.

-Liver transplantation: consists in replacing the cancerous liver by a healthy liver provided by a specific donor. Transplantation is generally used for unresectable (inoperable) liver tumors in patients with advanced cirrhosis.

-Local ablative therapy: used for tumors no larger than 3 cm across. The tumor is destroyed without removal, either by freezing, heat, electrical pulses or by the introduction of pure alcohol. Among local ablative therapies there is radiofrequency ablation where high frequency electrical currents are passed through a needle electrode resulting in a heat that is able to "burn out" the tumor [START_REF] Mcdermott | Radiofrequency Ablation of Liver Tumors[END_REF]. A non-invasive novel technique, called high-intensity focused ultrasound (HIFU), is also used for tumor ablation [START_REF] Zavaglia | High-intensity focused ultrasound (HIFU) for the treatment of hepatocellular carcinoma: is it time to abandon standard ablative percutaneous treatments?[END_REF]. The technique is based on directing an ultrasound beam to the targeted tissue, resulting in a local temperature increase that induces a coagulative necrosis without damaging the surrounding healthy tissue.

Unfortunately, in most cases, curative treatments cannot be performed, either because the cancer size has become too large or because it has spread in many different parts of the liver (i.e. intermediate or advanced stage cancer). These conditions lead to an inclination toward palliative therapies, having the potential to escalate survival and improve the quality of life.

Palliative therapies include:

-Radiotherapy [START_REF]Radiotherapy for liver cancer[END_REF]: Tumors are treated by high-energy rays delivered by special equipment. The radiation can affect healthy cells.

-Chemotherapy [START_REF]A prospective randomized clinical trial -Falksom -1978[END_REF]: Tumors are treated by the introduction (injected or ingested) of chemotherapeutic drug that is designed to attack the whole body or specific region fast-growing cells including cancer cells.

-Embolization [START_REF] Lencioni | Chemoembolization of Hepatocellular Carcinoma[END_REF], [START_REF] Sangro | Radioembolization for hepatocellular carcinoma[END_REF]: Tumors are selectively treated by the direct introduction of specific substances into specific liver artery.

-Targeted drugs: Tumors are treated by the introduction (injected or ingested) of targeted drugs that are designed to attack the whole body changed cells that cause cancer.

-Immunotherapy [START_REF] Greten | Targets for immunotherapy of liver cancer[END_REF]: Tumors are treated by a patient's own immune system that is boosted by some dedicated medicines. Immunotherapy for the treatment of various cancer types has advanced rapidly in recent years.

Some liver cancer treatments, in particular transarterial radioembolization, are performed by minimally invasive interventions, where interventional tool are introduced under X-Ray image guidance. In the following, we introduce the main principle of radioembolization treatment as well as the typical intervention guidance. Then, we present the proposed approach designed to improve the image guidance during the procedure.

Image-guided radioembolization

In this section, we present the radioembolization treatment characteristics and workflow, then, we describe the typical intervention image guidance and mention its related issues.

Transarterial radioembolization

Radioembolization (RE), or selective internal radiation therapy, is a minimally-invasive intervention performed to treat unresectable primary and metastatic liver cancer. The treatment relies mainly on the delivery of radioactive glass (TheraSphere, MDS Nordion, Toronto, Canada) or resin (SIR-Spheres, Sirtex Medical, Sidney, Australia) microspheres embedding either Yttrium-90 (90Y) or Holmium-166 (166Ho) isotope into hepatic tumors.

The small beads are selectively injected into the hepatic feeding arteries of the tumor by a trans-femoral catheter in order to retrograde the blood flow and emit a radiation dose that is able to treat the cancer cells. RE has been introduced after the emergence of hepatic arterydirected therapies for HCC in 1950s, when investigators found that hepatic tumors are mainly fed via hepatic arteries (80% of their blood supply is derived from the hepatic artery while normal parenchyma receives its blood from portal vein) [START_REF] Bierman | Studies on the blood supply of tumors in man. III. Vascular patterns of the liver by hepatic arteriography in vivo[END_REF]- [START_REF] Balogh | Hepatocellular carcinoma: a review[END_REF]. Due to its demonstrated safety and efficacy by extending the survival of patient to a median reaching 31.9 months [START_REF] Sangro | Radioembolization for hepatocellular carcinoma[END_REF], [START_REF] Sangro | Survival after yttrium-90 resin microsphere radioembolization of hepatocellular carcinoma across Barcelona clinic liver cancer stages: a European evaluation[END_REF]- [START_REF] Kwok | Survival benefit of radioembolization for inoperable hepatocellular carcinoma using yttrium-90 microspheres[END_REF], RE has been adopted across BCLC stages. Over recent years, it has emerged as a surrogate of Transarterial Chemoembolization (TACE) [START_REF] Lencioni | Chemoembolization of Hepatocellular Carcinoma[END_REF] [27], the standard therapy used for intermediate stage cancer (BCLC B). RE and TACE procedures mainly differ in terms of injected agent and interventional protocol.

In order to ensure patient eligibility for RE, a pretreatment assessment intervention consisting in catheterizing the arterial tree is performed one to two weeks before the actual therapy.

Indeed, a technetium 99mTc macro aggregated albumin (99mTc-MAA) is injected as a surrogate of the 90Y/166Ho to be able to precisely: locate the injection site, detect potential extrahepatic shunting and perform activity planning [START_REF] Garin | Personalized dosimetry and intensification concept with 90Y-loaded glass microsphere radioembolization induce prolonged overall survival in hepatocelluar carcinoma patients with portal vein thrombosis[END_REF]. During the pretreatment assessment intervention, the injection of 99mTc-MAA performed through a catheterization procedure plays an essential role in estimating the absorbed dose and activity value of the 90Y/166Ho microspheres. In fact, the 99mTc-MAA is used to predict 90Y/166Ho microspheres distribution during the treatment intervention. An accurate determination and localization of the injection site, in both pretreatment and treatment procedures, directly impact the treatment outcome.

To access the 99mTc-MAA injection site, an incision is carried out in the common femoral artery with a needle, and then a catheter sheath is inserted so the guidewire, catheter and micro-catheter can be introduced. A trans-femoral flexible catheter is introduced through the sheath with the guidance of a thin guidewire, threaded up to the aorta and selectively catheterizes the celiac trunk. A micro-catheter is then passed into the catheter, pushed through the common hepatic artery, and advanced eventually in the arterial supply of the tumor (Figure 10). The 99mTc-MAA substance is then delivered to the tumor via the inserted micro-catheter. The catheterization procedure and the confirmed injection site position are carefully reproduced during the treatment intervention for the delivery of 90Y/166Ho microspheres.

Based on the performed 99mTc-MAA distribution, a proper 90Y/166Ho activity is delivered.

Radioembolization has a low rate of serious reactions. Generally, patients may only experience some mild side effects like: abdominal pain, fever, nausea and fatigue, but they may be discharged from the hospital within a day or two after the procedure.

Typical radioembolization image guidance

The main goal of radioembolization is to deliver a customized 90Y/166Ho activity to the liver tumors with maximum radiation focus, allowing minimizing exposure for the remaining healthy liver parenchyma. The catheterization procedure is performed under image guidance

Computer-assisted radioembolization: clinical context & proposed approach 40 (Figure 11), as direct eyesight is not possible. In this subsection, we describe the typical radioembolization intervention using image guidance.

Figure 11 -Radioembolization image guidance.

To prepare the procedure, a prior evaluation of the tumor(s) (i.e. volume, location) is performed preoperatively using various image modalities [START_REF] Jafargholi Rangraz | Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization[END_REF], [START_REF] Spahr | Multimodal image registration for liver radioembolization planning and patient assessment[END_REF]. Preoperative images, particularly computer tomography angiography (CTA) and Magnetic Resonance Angiography (MRA) generally acquired within few weeks before the intervention, have the potential to identify significant information about the hepatic arterial tree, the tumor location and volume as well as the tumor feeding-vessels. Tumors and vessels are visualized by the administration of contrast agent. This information can help radiologists for disease diagnosis, treatment and follow-up.

The pretreatment and treatment interventions are performed in a room called interventional radiology suite (Figure 12), under X-Ray image guidance acquired with a C-arm system.

Such a system is able to acquire 2D and 3D reconstructed X-Ray images either with or without contrast agent administration.

Figure 12 -Interventional radiology suite (from [START_REF]Key Considerations for Your Interventional Radiology Suite[END_REF]).

We can divide the catheter manipulation guidance into five main parts:

1-The catheter is manipulated from the femoral artery until reaching the celiac trunk (i.e.

departure/ entrance of hepatic arterial tree) while observing predominantly 2D intrainterventional fluoroscopic images. Contrast product is frequently injected in order to localize the celiac trunk origin.

2-

The celiac trunk originates from the aorta and divides into the splenic artery and the common hepatic artery. In general, once the catheter is inserted in the celiac trunk, the radiologist performs a 2D DSA by injecting the contrast product in order to visualize the overall structure. The performed 2D enhanced vessel tree is used as a roadmap to assist the common hepatic artery access and to manipulate the micro-catheter inside the hepatic arterial tree.

3-At that level, the micro-catheter is inserted to be able to access the hepatic arterial tree.

The manipulation of the micro-catheter is performed under 2D fluoroscopic images.

For guidance, the radiologist mentally reconstructs the hepatic arterial tree previously visualized in the performed 2D DSA. Contrast product is still continuously injected to enhance the blood vessels and assist the catheter manipulation.

4-By reaching the injection site belonging to tumor feeding vessels, another 2D DSA is performed to ensure the location of this determined injection site and finally, 99mTc-MAA (during the pretreatment assessment intervention) or 90Y/166Ho ( during treatment intervention) substances are injected.

5-At the end, a CBCT is acquired to evaluate the perfused tissue and the tumoral targeting and lastly, the micro-catheter and the catheter are smoothly pulled off.

As part of treatment protocol, a 99mTc-MAA single-photon emission computed tomography (SPECT/CT) is acquired after the pretreatment assessment interventions for 99mTc-MAA distribution assessment and 90Y dosimetry prediction, performed by the calculation of the 90Y activity that would get an optimal tumor response [START_REF] Garin | Clinical impact of 99mTc-MAA SPECT/CT-based dosimetry in the radioembolization of liver malignancies with 90Y-loaded microspheres[END_REF]- [START_REF] Morán | Impact of the dosimetry approach on the resulting 90Y radioembolization planned absorbed doses based on 99mTc-MAA SPECT-CT: is there agreement between dosimetry methods?[END_REF]. A positron emission tomography with integrated computed tomography PET/CT [START_REF] Alsultan | The value of yttrium-90 PET/CT after hepatic radioembolization: a pictorial essay[END_REF] is acquired after the treatment intervention for the assessment of the treatment success by visualizing the 90Y microspheres distribution, tumor targeting, and quantifying the absorbed dose.

For follow-up, CTA and MRA are also acquired few months after radioembolization in order to ensure the treatment performance in treating lesions.

As described above, interventional radiologists are used to manipulate the catheter while observing predominantly 2D intra-interventional fluoroscopic images, along with 2D digital subtraction angiography images (DSAs) which are acquired to occasionally visualize the arterial tree. Due to the vasculature complexity, the catheter manipulation and more specifically the access to the celiac trunk origin as well as to the injection site, during pretreatment and treatment interventions, is challenging task. The contrast agent used to highlight vessels is relatively toxic and its continuous or excessive injection can cause severe reactions for the patient. Besides, the continuous radiation exposure for a long procedure can be harmful for both patient and radiologist. Reducing contrast agent use and radiation exposure is still highly dependent on the radiologist experience as well as his ability to understand the vasculature and efficiently manipulate the catheter inside.

Another challenge that can face radiologists is the reproduction of the injection site located during the pretreatment intervention in the treatment intervention. In order to prevent dose distribution deviations, the injection site should be reproduced as identically as possible. In general, the radiologist mentally re-locates the injection site while resorting to X-Ray images (i.e., fluoroscopy and DSA) acquired during the pretreatment assessment intervention when the catheter is being inserted in its final position. In this case, unmatched injection site locations risk is very high.

Proposed approach

In general, minimally-invasive interventions are performed under image guidance, as direct eyesight of the region to be treated is not possible. Radiologists have generally recourse to intraoperative medical imaging modalities such as X-Ray to manipulate their instruments inside the patient's body. Image guidance can be improved by augmented navigation process that takes benefits of the acquired image modalities or other available measuring devices. In neurovascular procedures, the fusion of preoperative images likes 3DRA, CT or MR with intraoperative fluoroscopic images allows more accurate guidance by providing detailed 3D information regarding the vascular morphology and pathology [START_REF] Ruijters | Validation of 3D multimodality roadmapping in interventional neuroradiology[END_REF]. In the context of structural cardiac interventions, the overlay of 3D CT data onto the real-time catheterization fluoroscopy proved to be feasible to aid procedure guidance [START_REF] Krishnaswamy | Integration of MDCT and fluoroscopy using C-arm computed tomography to guide structural cardiac interventions in the cardiac catheterization laboratory[END_REF]. Moreover, the fusion of echocardiographic and fluoroscopic images provides also precise catheter manipulations under fluoroscopy guidance by visualizing critical tissue anatomy [START_REF] Thaden | Echocardiographic and Fluoroscopic Fusion Imaging for Procedural Guidance: An Overview and Early Clinical Experience[END_REF]. Indeed, many approaches rely on the fusion of different image modalities in order to augment and complement the radiologist's ability to understand the spatial structure of the anatomy [START_REF] Maintz | A survey of medical image registration[END_REF]- [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF].

To our knowledge, no catheter navigation support approach dedicated to the RE intervention and its specific issues has been introduced in the literature so far. The purpose of our work, in this thesis, is to improve the image guidance in radioembolization interventions by assisting the celiac trunk and injection site access and minimizing the injection site reproduction inaccuracy. The proposed guidance strategy will be detailed in next chapters.

In the context of liver catheterization, some methods have been introduced to align preoperative 3D volumes with 2D intraoperative images using either rigid or deformable registration. Due to the respiration motion, the liver undergoes mainly a cranio-caudal translation [START_REF] Clifford | Assessment of hepatic motion secondary to respiration for computer assisted interventions[END_REF], [START_REF] Venkatraman | Liver Motion Due to Needle Pressure, Cardiac, and Respiratory Motion During the TIPS Procedure[END_REF], while deformation occurs mainly in the superior and inferior liver areas [START_REF] Blackall | A Statistical Model of Respiratory Motion and Deformation of the Liver[END_REF], [START_REF] Rohlfing | Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images[END_REF]. Thus, some guidance approaches are limited to rigid-body registration to align preand intraoperative hepatic structures. In the context of chemoembolization, which is similar to RE except the injected substances (i.e. combination of embolic and chemotherapeutic agents)

[27], Ambrosini et al. [START_REF] Ambrosini | Continuous roadmapping in liver TACE procedures using 2D-3D catheter-based registration[END_REF] designed a 3D/2D real-time rigid registration method between 3D rotational angiography (3DRA) and 2D fluoroscopy based on 3D extracted vessels and 2D

catheter shape. The study demonstrates that the accuracy is highly depending on the 2D catheter segmentation precision, which might fail due to the variety of surgical scenes. In order to be robust against the segmentation error, the authors proposed the use of a probabilistic model to track the catheter tip inside the patient's 3D vessel tree [START_REF] Ambrosini | A Hidden Markov Model for 3D Catheter Tip Tracking With 2D X-ray Catheterization Sequence and 3D Rotational Angiography[END_REF]. Bargellini et al. [START_REF] Bargellini | Image fusion of preprocedural CTA with real-time fluoroscopy to guide proper hepatic artery catheterization during transarterial chemoembolization of hepatocellular carcinoma: a feasibility study[END_REF] proposed a rigid registration technique of relevant 3D structures, segmented from a CTA volume, with the 2D intra-interventional fluoroscopy based on the lumbar spine. Their method demonstrates its benefit to provide confident hepatic arterial catheterization. The main drawback lies, nevertheless, in the fact that the proposed registration is performed manually.

Deformable registration methods have also shown promising results for aligning vessels despite excessive deformations due to artifacts and noise. Guevara et al. [START_REF] Garcia Guevara | Biomechanics-based graph matching for augmented CT-CBCT[END_REF] suggested a solution based on a biomechanical model in order to preserve the physical behavior of the liver and to avoid the inappropriate deformations. Their approach aims at aligning the preoperative CT with the intraoperative CBCT images, thanks to a graph matching technique.

However, the computation time (i.e. 7.3 min) makes the method not compatible with the context of interventional guidance.

To our knowledge, there is no X-Ray image augmentation solution for catheter manipulation and target site reproduction specifically dedicated to RE intervention guidance. In this context, we intend to develop and evaluate a novel guidance strategy that promises a simplification and accuracy of the catheter navigation during the pretreatment assessment, as well as during the treatment interventions. As part of RE clinical routine, 3D CTA is acquired to plan the intervention. The vascular system is visualized in this 3D image by the injection of contrast product. Our proposed navigation system processes preoperative (i.e. CTA) and intraoperative images to achieve intraoperative image fusion through a rigid registration technique. This approach is designed to: 1) assist the celiac trunk access, 2) assist the injection site access and 3) automatically reproduce the injection site during the proper intervention.

The three fusion steps are depicted in (Figure 13) and will be presented in the next two chapters. Image registrations are designed to guide the pretreatment assessment intervention by projecting the celiac trunk and the hepatic arterial tree on 2D X-Ray images and to guide the treatment intervention by reproducing the injection site path.

Augmenting 2D intraoperative X-Ray images, by projecting relevant information from either 3D preoperative CTA or pretreatment X-Ray images, has the potential to enhance the visual guidance while providing precision and safety to the catheter manipulation. However, due to respiration motion, the liver undergoes a continuous displacement [START_REF] Clifford | Assessment of hepatic motion secondary to respiration for computer assisted interventions[END_REF], [START_REF] Venkatraman | Liver Motion Due to Needle Pressure, Cardiac, and Respiratory Motion During the TIPS Procedure[END_REF], [START_REF] Rohlfing | Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images[END_REF], hampering the arterial tree static projection. In this context, our objective is also to deal with dynamic overlay of the projected 3D vessels onto fluoroscopy despite the respiration motion. 2D intraoperative fluoroscopic images provide basic information about the patient's anatomy (i.e. bone structure) and inserted interventional tools, whereas, as described in section 1.2.2. ), the nonpermanent vascular structure visualization is still dependent on contrast medium injection (i.e. angiographic images). Computer-aided image guidance solutions have been introduced to enable the enhancement of visualization and to provide a more comprehensive feedback of the surgical scene. In particular, these solutions enable the augmentation of intraoperative 2D X-Ray images (i.e. fluoroscopic and angiographic images) by combining them with 3D medical images, preoperatively acquired, using 2D/3D registration.

The registration of the two image modalities can be handled in different manners. In this chapter, we provide an overview of registration methods used to augment 2D X-Ray images and we expose the proposed registration framework (section 2.1.) being used in our computerassisted navigation approach. In sections 2.2. and 2.3., we present the first two fusion steps being designed to guide the pretreatment assessment intervention by assisting the celiac trunk access and the injection access, respectively.

Registration approach

The 2D X-Ray images intrinsic nature often requires the clinicians' capacity to mentally construct the 3D surgical scene. In fact, this procedure is not always reliable due to the anatomical variability. In order to conceive a better visualization of a specific patient surgical scene, many acquisitions from different angles may be performed by a C-arm before starting the intervention. However, these acquisitions might be time consuming and lead to an increased radiation exposure. In this context, many studies focus on taking advantage of preoperative 3D images and register them, either manually or automatically, to intraoperative X-Ray images in order to provide a more extensive view beyond the visible anatomy [START_REF] Sailer | CTA with fluoroscopy image fusion guidance in endovascular complex aortic aneurysm repair[END_REF]- [START_REF] Bargellini | Image Fusion of Preprocedural CTA with Real-time Fluoroscopy to Guide Proper Hepatic Artery Catheterization During Transarterial Chemoembolization of Hepatocellular Carcinoma: A Feasibility Study[END_REF] (an example is illustrated in Figure 14). The enhanced visualization by 2D X-Ray augmentation has the potential to reduce the intervention time, contrast medium injection and the radiation exposure. This has been demonstrated by [START_REF] Ahmad | Image fusion using the two-dimensional-three-dimensional registration method helps reduce contrast medium volume, fluoroscopy time, and procedure time in hybrid thoracic endovascular aortic repairs[END_REF], who performed a comparison between classic thoracic endovascular aortic repair procedures (TEVAR) and TEVAR procedures performed with 2D/3D image registration. and peripheral artery disease procedures (Figure 15).

Figure 14 -Early arterial phase CT images are post processed on an independent workstation by semiautomatic segmentation, obtaining the VR model of abdominal aorta and splanchnic arteries (A). The 3Dmodel is then manually registered to the real-time fluoroscopic image using the bone structures as reference (B), and it is used to catheterize the celiac trunk (C) and the proper hepatic artery (D, E). Only at this time is 3 mL of contrast medium injected to check the correct catheter position (F). It is possible to notice how the VR model allows visualizing the hepatic structure, not enhanced in 2D fluoroscopic images.

From [START_REF] Bargellini | Image Fusion of Preprocedural CTA with Real-time Fluoroscopy to Guide Proper Hepatic Artery Catheterization During Transarterial Chemoembolization of Hepatocellular Carcinoma: A Feasibility Study[END_REF] Pretreatment assessment intervention guidance 50

2D/3D Registration

2D/3D image registration can be defined as a search for the spatial transformation that brings the 3D preoperative data and 2D intraoperative data into the same coordinate system [START_REF] Markelj | A review of 3D/2D registration methods for image-guided interventions[END_REF].

Other than manual registrations which are imperfect by definition, an overview of automatic 2D/3D registration methods can be found in [START_REF] Markelj | A review of 3D/2D registration methods for image-guided interventions[END_REF]- [START_REF] Matl | Vascular image registration techniques: A living review[END_REF]. In the following, we summarize the classification of 2D/3D registration methods.

As presented by [START_REF] Markelj | A review of 3D/2D registration methods for image-guided interventions[END_REF], we can classify 2D/3D registration methods into calibration-based, extrinsic, or intrinsic. Calibration-based methods rely on the use of known device calibration to align the 2D intraoperative X-Ray image with the 3D preoperative image [START_REF] Ruijters | Validation of 3D multimodality roadmapping in interventional neuroradiology[END_REF], [START_REF] Atasoy | Real-time respiratory motion tracking: roadmap correction for hepatic artery catheterizations[END_REF]. This approach requires the use of same 2D and 3D image acquisition device.

Registration approach 51

Extrinsic methods generally rely on external markers, presenting external objects attached to the patient, for registration [START_REF] Pawiro | Validation for 2D/3D registration. I: A new gold standard data set[END_REF] while intrinsic methods rely on anatomical structures as bone structures or vascular structures to register images. We can further classify registration approaches into feature-based and intensity-based.

Feature-based approaches consist in computing a geometric distance between features (e.g. vessel centerlines, contours, bifurcations, etc.) extracted from 2D images and projected features of the 3D preoperative images [START_REF] Ambrosini | Continuous roadmapping in liver TACE procedures using 2D-3D catheter-based registration[END_REF], [START_REF] Ruijters | Vesselness-based 2D-3D registration of the coronary arteries[END_REF], [START_REF] Ma | Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures[END_REF]- [START_REF] Sundar | A novel 2D-3D registration algorithm for aligning fluoro images with 3D pre-op CT/MR images[END_REF]. Feature extraction usually requires a segmentation step. For example, in vessel based registration methods where vasculature is used for registration, a segmentation of the both vasculatures visualized in 2D intraoperative X-Ray image (i.e. angiography) and preoperative 3D image are required to extract some features like centerlines, contours and bifurcations before performing the registration. Feature-based approaches have proven to be accurate and fast since they don't evaluate a matching criterion on every image pixel, but rather rely on a relatively small number of features. However, these approaches can suffer from robustness issues depending on segmentation precision. In intraoperative X-Ray augmentation, anatomical structures may vary from patient to another, decreasing automatic segmentation performance accuracy.

Moreover, a manual segmentation process could be time consuming and hence, not compatible with the clinical workflow. This drawback can be partially overcome by intensitybased methods, which do not require any intraoperative segmentation step.

Intensity-based approaches count on all image pixels intensities by evaluating and optimizing a similarity measure between the intraoperative X-Ray image and the projection of the 3D

preoperative image [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF], [START_REF] Hipwell | Intensity-based 2-D -3-D registration of cerebral angiograms[END_REF]- [START_REF] Bom | Evaluation of optimization methods for intensity-based 2D-3D registration in x-ray guided interventions[END_REF]. Different similarity measures have been proposed and evaluated for this purpose [START_REF] Penney | A comparison of similarity measures for use in 2D-3D medical image registration[END_REF]. Intensity-based methods are also proven to be accurate but their accuracy can depend on the 3D preoperative image projection quality as evoked by [START_REF] Markelj | A review of 3D/2D registration methods for image-guided interventions[END_REF] and [START_REF] Matl | Vascular image registration techniques: A living review[END_REF], when there is no correspondence between intraoperative X-Ray and preoperative projection images. They might also be more computationally demanding and tend to converge slowly, preventing their integration into clinical workflow [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF], [START_REF] Kubias | Extended Global Optimization Strategy for Rigid 2D/3D Image Registration[END_REF].

Pretreatment assessment intervention guidance 52

Unlike feature-based registration approaches requiring intraoperative image segmentation which is a limitation for interventional application, our work will focus on investigating a rigid intensity-based approach.

Furthermore, to determine the performance, the limitations as well as the validity for clinical use of a proposed registration method, a rigorous evaluation experiment must be performed.

Validation can be performed using a qualitative or quantitative method to qualify or quantify the registration error [START_REF] Pluim | The truth is hard to make: Validation of medical image registration[END_REF]. Visual inspection consists in using structures overlays, side-by-side viewing or checkerboard displays [START_REF] Fitzpatrick | Visual assessment of the accuracy of retrospective registration of MR and CT images of the brain[END_REF], [START_REF] Denton | Comparison and evaluation of rigid, affine, and nonrigid registration of breast MR images[END_REF]. This type of evaluation is subjective and can be used for small database. Quantification methods can employ either ground truth or gold standard for validation. Ground truth presents an exact definition of the correct transformation and gold standard uses an approximation of it. The ground truth is hard to be defined and few methods can truly provide it. Validation approaches that are based on phantoms [START_REF] Roche | Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information[END_REF]or simulated images [START_REF] Rohr | Spline-based elastic image registration: integration of landmark errors and orientation attributes[END_REF] are able to potentially define a ground-truth, however, these methods suffer from lack of realism, making them less significant for clinical applications. Most of methods rely on gold standard for validation. Many gold standard based validation approaches consist in aligning corresponding anatomical structures belonging to the fixed and transformed images or volumes (after registration). This alignment can be defined by different quantitative measures such as overlap [START_REF] Song | Methods for evaluating image registration[END_REF]. This validation approach accuracy depends on the feasibility of defining these structures and their segmentation precision.

Validation can be also performed by measuring the distance between corresponding points that belong to the fixed and moving images. These points can correspond to anatomical landmarks or tailor-made markers. This validation method provides a direct measure of how well corresponding points line up after registration. However, it only measures registration accuracy at these specific points' locations (i.e. anatomical landmarks or tailor-made markers)

and not at non-points locations. Hence, a dense population of points is required to get a better estimation of the registration accuracy [START_REF] Song | Methods for evaluating image registration[END_REF].

In our context, we will favor a gold standard based approach to evaluate the registration method performance.

In the next subsections, we present the proposed registration approach and provide the validation method details.

Proposed multiresolution intensity-based registration

In our work we propose to investigate an optimized structure-driven intensity-based approach to perform preoperative and intraoperative image fusion (Figure 16). In order to improve the registration, this approach consists in segmenting a 3D preoperative CTA before the intervention in order to generate a projection image, a Digitally Reconstructed Radiograph (DRR), corresponding to the anatomical structure visible in the real X-ray image (Figure 17).

The CTA volume segmentation is performed using Endosize®, THERENVA sizing software [START_REF] Kaladji | Sizing for endovascular aneurysm repair: clinical evaluation of a new automated three-dimensional software[END_REF] (Figure 18).

Figure 16 -Intensity-based registration framework with its main components. Preoperative arterial phase Computed Tomography (CTA) images are semi-automatically segmented using endosize®, obtaining either a 3D bony-structure or delimited vascular-structure (1). DRR image is generated from the preprocessed volume (2) and compared to the 2D intraoperative image through a similarity metric presenting a quantitative criterion describing how well the input images are similar (3). For optimization, the 3D volume is intermediately transformed

following an exhaustive search and the transformation result is progressively refined following a multiresolution scheme [START_REF] Brunicardi | Schwartz's principles of surgery[END_REF]. The final 3D/2D transformation corresponds to the transformation giving the best achieved similarity measure [START_REF]Overview of Blood Vessel Disorders of the Liver -Liver and Gallbladder Disorders[END_REF]. The registration algorithm is based on an optimization by a reduced exhaustive search involving a multi-resolution scheme with a decomposition of the transformation in order to reduce the computation time, making it compatible with clinical workflow.

Considering predefined ranges of unknown parameters and a rough initialization by the user, the multi-resolution algorithm, originally introduced in the context of endovascular aortic aneurism repair [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF] and successfully integrated in EndoNaut® device, is implemented to perform an exhaustive search of the pre/intraoperative transformation. The optimal 3D/2D transformation is obtained following a multi-resolution coarse-to-fine registration between synthetic 2D X-Ray images (i.e. DRRs) generated from the 3D preoperative volume and intraoperative real 2D X-Ray image (i.e. fluoroscopy or DSA) acquired by a C-arm. DRRs are generated from the preprocessed CTA by using the projection inherent to the fluoroscopic imaging system (known intrinsic parameters) and approximating X-ray absorption process by volume rendering with ray-tracing [START_REF] Kim | Fast 2D-3D registration using GPU-based preprocessing[END_REF].

The multi-resolution scheme consists in progressively refining the transformation result. At each resolution level, the optimal transformation is estimated by performing a reduced exhaustive search while decomposing the transformation T into in-plane and out-planes transformations, as introduced in [START_REF] Kubias | Extended Global Optimization Strategy for Rigid 2D/3D Image Registration[END_REF]. The parameter space of each transformation is sampled on a regularly discretized range and the different combinations of parameters result in transformations such that:

( )
where correspond, respectively, to the translation and rotation parameters of the outplane transformation applied on the 3D volume and refer to the parameters of the in-plane transformation applied to obtain the resulting DRR (Figure 19). A similarity measure is computed between the intraoperative 2D image and the DRR for each combination of the parameters. The optimal transformation corresponds to the combination of parameters giving the best similarity measure outcome. It is used as the initial solution for the next resolution level. is the world coordinate system, is the intraoperative image coordinate system, is the preoperative volume coordinate system and is the coordinate system where:

Pretreatment assessment intervention guidance

is the plane parallel to the plane of intraoperative image and is aligned with the main axis of c-arm. and correspond to in-plane translations and corresponds to the out-plane translation (along the axes of ). and correspond to the out-plane rotations and corresponds to the inplane rotation (around the axes of )

In the following, the implementation of this method and obtained results are presented for the celiac trunk access and the injection site access respectively.

Celiac trunk access

Given the celiac trunk catheterization complexity, radiologists often resort to repeated contrast product injection to visualize the vasculature that is close to the catheter (about 15 mL for each DSA acquisition). The celiac trunk origin visualization through real-time fluoroscopic images augmentation has the potential to ease the catheterization while reducing the contrast medium injection. According to a study performed by Sailer et al. [START_REF] Sailer | Quantification of Respiratory Movement of the Aorta and Side Branches[END_REF] on 60 different CT scans acquired during inspiration and expiration, abdominal side branches (e.g.

We have evaluated the impact of different similarity measures [START_REF] Penney | A comparison of similarity measures for use in 2D-3D medical image registration[END_REF] on the registration outcome. Gradient information (GI), gradient difference (GD), gradient correlation (GC), normalized cross correlation (NCC) and Mutual information (MI) have been tested. For each patient case, evaluation process was performed in four steps: defining a gold standard (GS) registration, generating random initial transformations, performing corresponding registrations and computing a mean error. The steps are described in detail below. In the first step, the GS was obtained by a manual registration (between the preoperative CTA and the intraoperative fluoroscopic image) carefully performed by an expert and used as a reference transformation for the evaluation protocol. Fifteen initial transformations were randomly generated within a defined range away from the assumed GS in order to analyze the influence of the rough manual initialization required from the user. This defined range was fixed to ±10 mm for each of the three translational axis and ±4 degrees for each of the three angles. The registration was then performed from each random initial transformation. The registration accuracy was expressed by the mean error representing the mean 2D Euclidean distance calculated on the registrations of one patient case and the global mean error representing the mean 2D Euclidean distance calculated on all patient cases' registrations. The mean 2D Euclidean distance was computed from all the points belonging to the considered anatomical structure (i.e. bony-structure) obtained by applying the reference transformation and their homologues obtained by applying the resulting transformation. Registration success rate was defined as the number of registrations showing a mean 2D Euclidean distance below a certain threshold T. The threshold was fixed to 5 mm according to a visual assessment of the results.

Following the registration, the 3D vascular-structure (i.e. the AA and its side branches origins) was transformed and projected onto the intraoperative fluoroscopic image in order to assist the catheterization. At this stage, a qualitative evaluation was carried out by an expert to assess the final rendering. We used 2D fluoroscopies captured after the catheterization of the celiac trunk origin for evaluation. For each patient, the expert visually inspected the position of the celiac trunk origin (ostium region visible in the projected 3D vascular structure) and the catheter (visible in the 2D fluoroscopy) by verifying if an overlap occurs between them. The underlying assumption was that this overlapping is a necessary condition for the proposed approach to be efficient in reaching the celiac trunk origin with no need for intra-arterial contrast injection or DSA acquisition.

In the following, we present the clinical data used for the evaluation and report the 3D/2D registration results related to the celiac trunk access.

Clinical data

The overall approach was tested and evaluated on patient cases who underwent 90Y RE interventions between August 2014 and February 2019 at Centre Eugène Marquis (Rennes, France). Multiple factors were taken into consideration for patient selection as described in [START_REF] Wehrenberg-Klee | Patient Selection and Clinical Outcomes of Y90 in Hepatocellular Carcinoma[END_REF]. This was an observational study for which 20 patient cases were retrospectively included

Results

In (Figure 20), we show the bone-based rigid registration method results when varying initial conditions (i.e. 15 random initial transformations for each of the 20 considered patient cases) for the different similarity measures. The mean error varies from 1 mm to 60 mm. Best results are obtained by using GD, with a global mean error of 5.23 mm and a median at 1.40 mm. Results were less notable when using GI, GC, NCC or MI with a maximum success rate of 214/300 achieved when using GI. The average computation time for one registration was 5s on an integrated Intel graphic GPU Nvidia quadro k420 machine. The projection of the AA and side branches origins, which are resulted from the bone-based registration using the GD are however significant. In 83% of these registration cases, the expert confirmed that the visual result is substantial and the projected structure can

Pretreatment assessment intervention guidance

Injection site access

At the level of the celiac trunk, a micro-catheter is generally passed through the common hepatic artery (CHA), advanced in the proper hepatic artery (PHA) and eventually inserted in the right or left hepatic artery (RHA or LHA respectively) depending on the location of the tumor. The hepatic arterial tree projection is chiefly proposed to augment the intraoperative fluoroscopic images and the low-contrast DSA generally acquired at the level of the celiac trunk, and to simplify the access to the injection site in the midst of the vasculature complexity (Figure 22). Accordingly, we have taken advantage of the 3D/2D rigid registration method previously introduced to align the hepatic vascular-structure extracted from the CTA with the intraoperative DSA. In the following, we present the clinical data used for the evaluation and report the 3D/2D registration results related to IS access.

Clinical data

This was an observational study for which 19 patient cases were retrospectively included and randomly selected according to the data availability. Patient informed consent was obtained for being registered anonymously in the research database.

DSAs are provided within the standard imaging protocol and were acquired intraoperatively on a C-arm Artis Zeego (Siemens Healthcare, Forchheim, Germany) with 1149 mm source to detector distance and 0.3729 x 0.3729 mm pixel size. These intrinsic parameters have been used for the generation of DRRs. Each DSA is acquired after the injection of 15 mL iodine.

Results

The vessel-based rigid registration method is tested for two different configurations by considering the HVS and the HVSS. In Figure 24, we show the vessel-based rigid registration method results when varying initial conditions (i.e. 15 random initial transformations for each of the 19 considered patient cases) for the different similarity measures while keeping HVS as input vascular-structure.

By considering HVS, the mean error varies from 1 mm to 100 mm (obtained when the registration is unsuccessful). Best results are obtained by using GD, with a global mean error of 3.87 mm and a median at 2.35 mm.

Figure 24 -Boxplot of the mean errors obtained with each SM considering only HVS for the 19 patient cases

Table 4 shows the registration results when varying the similarity measure by considering the threshold T = 5 mm, while keeping HVS as input structure. 25) shows the registration result when using GD and NCC.

The average computation time for one registration, while using HVS with GD, was 25s.

The second experiment is performed to see how much the registration result can be influenced by the input vascular-structure. In this case the HVSS was used, along with the finest similarity measure (i.e. GD). By adding the splenic to the input vascular-structure, we have observed a decrease of the success rate to 68 out of 285 registrations, highlighting the dependence of the registration precision to the used input structure. A good anatomical correspondence is obtained when projecting the hepatic arterial tree (c). The registration result is degraded with NCC (d).

Discussion and conclusion

Manipulating the catheter and mapping the hepatic arterial tree remains a challenging task. Up to now, radiologists refer to their capacity to mentally construct a 3D vasculature from repeated 2D angiogram acquisitions in order to access the injection site. A poor perception of the anatomical structures can result in inadequate deposition of 90Y/166Ho agents in the considered lesion. In this chapter we have proposed a computer-assisted guidance strategy designed to progressively augment the intraoperative X-Ray image. A structure-driven intensity-based rigid registration method is used at two different stages of the RE intervention to augment the intraoperative image with additional relevant information. First, the aorta and the origins of its side branches are projected on the intraoperative 2D fluoroscopy following a 3D/2D bone-based registration process, to assist the celiac trunk access. Subsequently, a similar approach consisting in projecting the hepatic vasculature on intraoperative DSA through 3D/2D vessel-based registration is performed to assist the injection site location. This approach is able to ensure a reliable catheter manipulation and has the potential to reduce the contrast product administration and the radiation exposure.

The two fusion steps (i.e. bone-based registration and vessel-based registration) presented in this chapter were independently evaluated on subsets of 20 and 19 patient cases, respectively.

The approach resulted in qualitatively appropriate anatomical correspondences when projecting the preoperative structures on intraoperative images. With the best configuration, the registration steps showed accuracy and feasibility in aligning data, with global mean landmarks errors of 1.59 mm and 2.32 mm, respectively, a computation time that never exceeded 5 s and 25 s, respectively, and a user interaction limited to manual initialization of the 3D/2D registration. The preprocessing of the CTA can be performed before the scheduled procedure and may not require more than 10 min.

The accuracy and precision of the two fusion steps would benefit from the similarity measure selection. In this context, gradient information, gradient correlation, normalized cross correlation and mutual information, produced relatively dissatisfying results, whereas gradient difference (GD) yielded better robustness so far (i.e. success rate of 274/300 registrations where mean 2D Euclidean distance <5 mm for the bone-based registration and success rate of 239/285 registrations where mean 2D Euclidean distance <5 mm for the vessel-based registration).

Regarding the vessel-based registration, we assume that non-rigid deformations are negligible for two reasons: the central part of the liver (where the injection site is generally located) is almost rigid and the flexible micro-catheter pressure is not able to deform the arteries. Taking advantages of a vascular-structure that is the least likely to deform (i.e. HVS) enables the registration method to be performed correctly. It was noticed that the use of HVSS as a preoperative input structure, containing the deformable splenic artery which does not generally appear in the DSA, deteriorates the registration. To properly define the preoperative structure, it is necessary to favor the rigid structures as well as the structures which appear in the intraoperative image, such as HVS.

The treatment intervention is generally performed one to two weeks after the pretreatment assessment intervention for the delivery of 90Y/166Ho microspheres. These therapeutic microspheres are selectively injected into the hepatic feeding arteries of the tumor in order to retrograde the blood flow and emit a high radiation dose that is able to treat the cancer cells.

90Y/166Ho injection site is located beforehand during the pretreatment assessment intervention and its position is validated by the administration of 99mTc-MAA and SPECT/CT acquisition, as evoked in Chapter 1. An optimal determination of the injection site results in an adequate deposition of Y90/166Ho in the considered cancer tumor(s). Hence, the confirmed injection site has to be carefully reproduced during the treatment intervention for the delivery of a proper Y90/166Ho activity.

Similar to pretreatment assessment intervention, the treatment intervention is performed in an interventional radiology suite under X-Ray image guidance acquired with a C-arm system, providing insufficient information for intraoperative catheter manipulation.

In addition to catheter manipulation challenges which we have dealt with in Chapter 2, radiologists find difficulties in reproducing the injection site. In fact, in order to prevent dose distribution deviations, the injection site should be reproduced as identically as possible.

In this chapter, we intend to introduce and evaluate the third fusion step integrated within our guidance strategy, proposed to simplify the catheter manipulation and automatically reproduce the injection site during the treatment intervention.

2D/2D Registration approach

During the treatment intervention, radiologists generally re-locate the injection site (IS) while mainly resorting to fluoroscopic images acquired during the pretreatment assessment intervention when the catheter is being inserted in the IS (Figure 26).

Besides catheter manipulation requiring, in this case, the radiologist's ability to understand the vasculature and efficiently manipulate the catheter inside, a misunderstanding of the anatomy can result in inappropriate IS reproduction and thus an inadequate deposition of 90Y/166Ho agents in the considered lesion. Catheter manipulation assistance can be performed by augmenting intraoperative 2D X-Ray images by the corresponding anatomical structures extracted from preoperative CTA through 2D/3D intensity-based registration as described in Chapter 2, especially that this approach showed accuracy and feasibility.

Localizing the IS in the 3D CT volume from the 2D pretreatment assessment fluoroscopic image through back-projection has the potential to effectively reproduce the IS.

Knowing that the C-arm angulation remains the same in the two interventions (pretreatment assessment, treatment delivery) and in order to avoid performing an additional backprojection step, in our work, we propose to align the 2D pretreatment fluoroscopic image with the intraoperative 2D treatment fluoroscopy by performing a 2D/2D registration (Figure 27).

Subsequently, in order to have a more comprehensive rendering, the catheter is segmented from the moving image (i.e. pretreatment registered fluoroscopy) and reproduced onto the intraoperative image. At this stage, the catheter segmentation has been performed manually.

The catheter visualization could simultaneously and in a reduced time help on specifying the catheter path, the celiac trunk origin position and the IS location, presenting a substantial added-information. approaches consist in extracting a set of distinguishable geometrical features such as points of interest [START_REF] Harris | A Combined Corner and Edge Detector[END_REF], dots [START_REF] Marchand | VISP: a software environment for eye-in-hand visual servoing[END_REF], segments [START_REF] Marchand | VISP: a software environment for eye-in-hand visual servoing[END_REF]- [START_REF] Boukir | A local method for contour matching and its parallel implementation[END_REF], straight lines, ellipses [START_REF] Vincze | Robust tracking of ellipses at frame rate[END_REF], contours [START_REF] Berger | How to track efficiently piecewise curved contours with a view to reconstructing 3D objects[END_REF] and angles from the current frame and matching them with features extracted from previous frame through a specific matching approach such as RANSAC. In some complex situations, more enhanced descriptors combining these features with other types of visual information can be used.

2D intensity-based registration approaches [START_REF] Martinez | HMPMR strategy for real-time tracking in aerial images, using direct methods[END_REF] consist in minimizing the error measurement (by either minimizing or maximizing a similarity measure) that is based on the intensity values to estimate the transformation that align each consecutive frame pair.

Thanks to the efficiency of the multi-resolution intensity-based approach previously demonstrated (chapter 2), in this chapter we propose to tailor the 3D/2D registration framework to align the 2D pretreatment fluoroscopic image with the intraoperative 2D treatment fluoroscopy (Figure 28).

Figure 28 -Intensity-based registration framework with its main components. 2D pretreatment assessment and treatment fluoroscopic images are enhanced through preprocessing (1). 2D pretreatment assessment fluoroscopy is compared to the 2D intraoperative treatment fluoroscopy through a similarity metric presenting a quantitative criterion describing how well the input images are similar (2). For optimization, the 2D pretreatment assessment fluoroscopy is intermediately transformed following an exhaustive search and the transformation result is progressively refined following a multiresolution scheme [START_REF] Sutherland | Claude Couinaud: A Passion for the Liver[END_REF]. The final 2D/2D transformation corresponds to the transformation giving the best achieved similarity measure [START_REF] Brunicardi | Schwartz's principles of surgery[END_REF] Similarly to the 3D/2D registration, we considered an optimized intensity-based 2D/2D registration approach performed following a multi-resolution scheme (Table 1). The algorithm is implemented to perform an exhaustive search of the pretreatment and treatment transformation. The optimal 2D/2D transformation is obtained following a multi-resolution coarse-to-fine registration between pretreatment and treatment fluoroscopic images acquired by a C-arm.

The multi-resolution scheme consists in progressively refining the transformation result. At each resolution level, the optimal transformation is estimated by performing a reduced exhaustive search that is limited to in plane transformation (with parameters , subsection 2.1.2), that can be induced by the C-arm/patient positioning. Since the translation along y axis results in a scale factor in the 2D image plane, is also considered to be an unknown parameter (scale parameter) in the registration process. It allows taking into account potential C-arm zoom actions or table height changes [START_REF] Lalys | A hybrid image fusion system for endovascular interventions of peripheral artery disease[END_REF]. The parameter space of each transformation is sampled on a regularly discretized range and the different combinations of parameters result in transformations such that:

In order to measure the transformation result accuracy by considering a specific similarity measure, a global mean error corresponding to a mean Euclidean distance has been calculated between the corresponding defined 2D points.

In the following, we present the clinical data used for the evaluation and report the 2D/2D registration results related to IS reproduction.

Clinical data

The proposed approach was tested and evaluated on 5 patient cases who underwent 90Y RE interventions between August 2014 and February 2019 at Centre Eugène Marquis (Rennes, France). Multiple factors were taken into consideration for patient selection as described in [START_REF] Wehrenberg-Klee | Patient Selection and Clinical Outcomes of Y90 in Hepatocellular Carcinoma[END_REF]. The 2D X-Ray images (i.e. fluoroscopy) are provided within the standard imaging protocol. They were acquired intraoperatively during the pretreatment assessment intervention and the treatment intervention on a C-arm Artis Zeego (Siemens Healthcare, Forchheim, Germany) with 1149 mm source to detector distance and 0.3729 x 0.3729 mm pixel size.

Results

In order to evaluate the method performance, an available sub-dataset of five fluoroscopic image pairs has been used.

In (Figure 29), we show the 2D/2D image-based rigid registration method results when varying similarity measures. For all image pairs, and by calculating the Euclidean distance between corresponding marked points, we have observed that the global mean landmark error varies from 1.08 mm to 3.51 mm. The method was successful on registering images when using GD with a global mean landmark error of 1.08 mm, calculated on all patient cases. The average computation time for one registration was 11 s on an integrated Intel graphic GPU Nvidia quadro k420 machine.

Figure 29 -Boxplot of the mean landmark errors obtained with GD for image-based registrations

As shown in (Figure 30) the registration does not seem to be disrupted by the presence of the catheter presenting in the pretreatment captured fluoroscopy.

In order to have a comprehensible rendering, we have manually segmented the catheter from the moving image (i.e. pretreatment registered fluoroscopy) and reproduced it onto the intraoperative image (Figure 31). The catheter visualization could help on specifying the catheter path, the celiac trunk origin position and the IS location, presenting a substantial added-information. image-based registration between pretreatment and treatment fluoroscopic images. This approach was evaluated on a subset of 5 patient cases. The fusion step showed accuracy and feasibility in aligning data, with global mean landmarks error of 2.17 mm, a computation time that never exceeded 11 s, and without any user interaction.

Through performed experiments, we observed that the accuracy and precision of 2D/2D registration would benefit from the similarity measure selection. Gradient information, gradient correlation, normalized cross correlation and mutual information, produced relatively dissatisfying results with our data, whereas gradient difference yielded better robustness so far (i.e. global mean landmark error of 1.08 calculated on five patient cases).

By using the gradient difference, the 2D/2D rigid transformation model has proven to be sufficient on registering pretreatment assessment and treatment fluoroscopic images. The projection of the catheter, as shown in this chapter, seems to be accurate to assist the catheter manipulation and reproduce the IS and therefore, reduce the contrast product administration, the radiation exposure and inadequate deposition of 90Y/166Ho risk.

To summarize the computer-assisted navigation approach developed in this thesis until now, The roadmap real-time repositioning can be handled in different manners. In this chapter, we provide an overview of dynamic roadmap approaches for improved image-guidance and we expose our proposed approach being used to reposition the hepatic arterial tree in the intraoperative 2D fluoroscopic sequence.

Dynamic roadmap correction

Radioembolization intervention is performed under live 2D X-Ray fluoroscopy guidance, displaying the catheter movement in real-time (7-8 frames per second) without the visualization of arteries, unless a contrast medium is injected. With the projection of a static roadmap of hepatic vasculature resulting from the registration of preoperative CTA and intraoperative angiographic images, the catheter is almost displayed outside the projected vasculature, due to the respiratory motion causing a continuous vasculature displacement.

Despite the advantages of the static projection of the hepatic arterial tree, interventional radiologists still have to mentally reposition this roadmap while navigating the catheter.

Real-time visual integration of projected roadmap on live 2D X-Ray fluoroscopy can further facilitate the catheter navigation procedure by allowing for continuous visualization of the hepatic arterial tree without injection of contrast medium. Our method should estimate the vessels motion through appropriate information and correct the hepatic roadmap with accuracy and speed, as two important aspects of performance in the context of RE image guidance.

Related works

Continuous roadmapping approaches have been introduced for the guidance of interventions especially in which static roadmaps are hampered by respiratory motion. YingLiang Ma et al. [START_REF] Ma | Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures[END_REF] proposed initializing the cardiac anatomical roadmap by using manual calibration-based methods and correcting the respiratory motion by using some specific features such as: heart border, diaphragm, tracheal bifurcation and catheter. Except the catheter, only the diaphragm structure in visualized in radioembolization 2D X-Ray fluoroscopy. Yang et al. [START_REF] Yang | Is Diaphragm Motion a Good Surrogate for Liver Tumor Motion?[END_REF] have analyzed the correlation between the liver tumor and diaphragm motion in three directions (SI, AP, and ML). Their study led to the fact that the diaphragm motion can be a good surrogate of the liver tumor motion in only SI and AP directions, and this concordance is affected by the distance between the tumor and the diaphragm. In the other hand, Davies et al. [START_REF] Davies | Ultrasound quantitation of respiratory organ motion in the upper abdomen[END_REF] have calculated displacement values of the entire liver and the diaphragm in SI direction during the respiration and concluded that for a same patient the displacement value of the diaphragm is different from that of the liver (e.g. 12±2 vs. 18±3 mm, respectively). Through these studies, it can be concluded that the diaphragm provides inappropriate information for estimating the liver motion and correcting the hepatic roadmap in radioembolization context.

The roadmap position update based on catheter information has been a center of interest for many researchers. In percutaneous coronary interventions, to perform dynamic coronary roadmapping on free breathing, Hua Ma et al. [START_REF] Ma | Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering[END_REF] proposed to compensate the respiratoryinduced vessel motion via catheter tip tracking in X-Ray fluoroscopy. Their roadmapping approach was validated on clinical data, achieving accurate performance.

In the context of chemoembolization, Ambrosini et al. [START_REF] Ambrosini | Continuous roadmapping in liver TACE procedures using 2D-3D catheter-based registration[END_REF] have proposed a real-time catheter segmentation followed by 2D/3D registration for continuous roadmaping (Figure 33). Their registration approach is highly dependent on the segmentation accuracy, a complex task by definition especially for intraoperative variable scenes. Another source of error is related to that their registration is performed on a vessel centerline running from the aorta to the hepatic vessel extremity, unsuccessful for cases presenting a lack of information in 3DRA (missing aorta and hepatic artery) or 2D X-Ray (when only short part of the catheter is visible). In order to correct the roadmap for hepatic artery catheterizations, Atasoy et al. [START_REF] Atasoy | Real-time respiratory motion tracking: roadmap correction for hepatic artery catheterizations[END_REF] proposed a semiautomatic method consisting in 1) performing a rigid 2D/3D pre-registration using machine calibration, 2) tracking the catheter by following one moving region of interest belonging to the 2D catheter visualized in 2D X-Ray and manually selected by the physician, and 3) updating the registration with this information. The tracking is performed using template matching with a translational motion model (Figure 34). Despite the manual interaction which is imperfect by definition, their motion model limited to in-plane translation has been successful in correcting the displacements caused by the breathing motion.

Liver motion and arterial deformation

The knowledge of a model for the liver displacement under respiratory motion can provide an important prior to estimate the motion of the liver vessels, and various studies have been performed to determine such a model. Korin et al. [START_REF] Korin | Respiratory kinematics of the upper abdominal organs: a quantitative study[END_REF] suggested that clinically significant liver motion could be effectively approximated by only a cranio-caudal movement while completely neglecting other axis of motion. This assumption was sustained by Davies et al. [START_REF] Davies | Ultrasound quantitation of respiratory organ motion in the upper abdomen[END_REF].

Considering these studies and knowing that during radioembolization the patient is posed in supine position and the C-arm is set in anterior-posterior position (Figure 35), we approximate the displacement of the hepatic arterial tree due to the respiratory motion in 2D X-Ray fluoroscopy using a translational model that covers translations in x and y directions in the image plane. Assuming that the deforming effect of respiration on liver's central part is negligible, our approach does not take into consideration deformations.

Catheter tip tracking

Approximation of vessel motion by catheter motion

In radioembolization, as detailed in subsections 1.2.1 and 1.3.1, the trans-femoral catheter is threaded up to the aorta and selectively catheterizes the celiac trunk. The micro-catheter is then passed into the catheter, pushed through the common hepatic artery, and advanced eventually in the arterial supply of the tumor (Figure 36).

Figure 36 -Liver catheterization procedure in radioembolization

Since the catheter is located within the celiac trunk, we assume that its motion replicates the hepatic vasculature motion. In our study we attempt to approximate the hepatic vasculature motion in X-Ray fluoroscopy from the catheter displacement. Hence, in this work, we are interested in proposing a method that is able to track the catheter tip visualized in 2D fluoroscopic image sequences (Figure 37). The computed catheter motion can be then transferred to the projected static roadmap to adjust it and achieve more realistic vasculature visualization with respect to the current catheter position.

In this context we have to deal with two challenges: The proposed method must be robust to degraded imaging conditions (noise, low contrast) when tracking the catheter tip, and the location must be provided in real time with respect to the C-arm acquisition rate.

Interventional tools tracking

Object tracking is one of the main research fields in computer vision, and is employed in many applications. The overall objective is to automatically identify and locate one or more targets from image sequence data. Since the application scenarios can be variable and sometimes complex, balancing the relationship between accuracy and computing time remains a difficult task.

Several studies have been performed to address the problem of interventional tools tracking in medical image sequences. Hua Ma et al. [START_REF] Ma | Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering[END_REF] have classified tracking methods into temporal tracking and tracking by detection approaches. Temporal tracking consists in locating an object or shape in an image by relying on its state in the previous images, by matching local features or global appearance between successive frames. In this context, Ambrosini et al [START_REF] Ambrosini | A Hidden Markov Model for 3D Catheter Tip Tracking With 2D X-ray Catheterization Sequence and 3D Rotational Angiography[END_REF] used a Hidden Markov Model (HMM) to track the catheter tip inside a 3D vasculature.

Speidel et al. [START_REF] Speidel | Tracking of Instruments in Minimally Invasive Surgery for Surgical Skill Analysis[END_REF] have used particle filters to track surgical instruments in medical images. In order to track multiple instrument regions simultaneously, Speidel et al. [START_REF] Speidel | Visual tracking of da Vinci instruments for laparoscopic surgery[END_REF] used multiobject particle filters, in which a particle represents a concatenation of several objects states.

The main advantage of temporal tracking approaches relies on reducing the detection search space in each frame, which makes tracking faster, whereas, a disadvantage of these methods rely on the error accumulation which can be produced by a tracking errors in each frame.

Instead, tracking by detections methods handle the problem as an object detection problem in each individual frame without considering previous frames outputs which prevent error accumulation caused by temporal tracking methods. In this context, Atasoy et al [START_REF] Atasoy | Real-time respiratory motion tracking: roadmap correction for hepatic artery catheterizations[END_REF] have proposed template matching to track the catheter in X-Ray fluoroscopic sequence frames.

Convolution neural networks have emerged as solution for various visual tasks. They have received recently significant attention in medical image processing community due to their demonstrated potential for segmentation [START_REF] Ronneberger | U-Net: Convolutional Networks for Biomedical Image Segmentation[END_REF], recognition [START_REF] Twinanda | EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos[END_REF]. They have also proven to be effective in interventional instruments detection [START_REF] Sahu | Addressing multi-label imbalance problem of surgical tool detection using CNN[END_REF] [104] [START_REF]CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes[END_REF]in particular.

Some researchers suggest a combination of temporal tracking and tracking by detection. For example, in a recent work published in the context of PCI by Hua Ma et al. [START_REF] Ma | Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering[END_REF], authors combined particle filter with deep learning to track the catheter tip in X-Ray fluoroscopy.

In our context, the challenge especially lies in dealing with the appearance of the guiding catheter tip used in radioembolization fluoroscopic image, which consists in a thin dark tubular shape showing few prominent features (Figure 37). Hence, following the deep learning paradigm, we present, in this chapter, a novel hepatic arterial tree repositioning technique in intraoperative X-Ray fluoroscopy, based on real-time catheter tip tracking. In order to overcome these issues, our method relies on CNN to detect the catheter tip in each frame.

A CNN based tracking

The recent success of deep learning in computer vision has given rise to the development of methods based on Convolutional Neural Networks (CNN) for object detection, providing predictions of object class labels and bounding boxes in images. Among popular techniques can be mentioned the Region CNN based models (R-CNN [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF], Fast R-CNN [START_REF] Girshick | Fast R-CNN[END_REF], Faster R-CNN [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF]) (Figure 38) which rely on a set of extracted or learned and predicted relevant region proposals which are more likely to contain the object, and which have proven to be fast, especially Faster-RCNN. Classifying the object presence in these regions is performed over features that are extracted by passing the regions through a CNN, while fitting a bounding box around the region proposals. Instead, the YOLO (You Only Look Once) approach, introduced by Redmon and al. [START_REF] Redmon | You Only Look Once: Unified, Real-Time Object Detection[END_REF] (Figure 39), does not process regions to localize the object but builds up a set of fixed anchor bounding boxes over each cell of a regular grid in the image, and provides a class probability and an offset for each bounding box, by passing through a single convolutional network. The object is then located by selecting the bounding boxes with probabilities over a certain threshold. W.r.t the R-CNN family, YOLO, as a single stage process, has proven to be faster and can be slightly more accurate [START_REF] Du | Understanding of Object Detection Based on CNN Family and YOLO[END_REF]. It is however less robust in detecting neighbouring multiple instances, especially in the presence of background clutter. However, the problem consisting in detecting a single small catheter tip within an image showing few salient background features, and in providing predictions in real-time within the sequence, we have employed the YOLO method in this work.

Experiments

We suppose that a continuous localization of the catheter tip may allow for continuous update of the hepatic arterial tree position. We assume that the link between the two structures (catheter tip and hepatic arterial tree) is maintained in the presence of the movement resulting from the respiratory motion. In this subsection, we present the qualitative and quantitative results obtained on real fluoroscopic image sequences for both catheter tip detection and hepatic arterial tree repositioning, in the context of radioembolization interventions.

Catheter tip detection evaluation metric

To evaluate the catheter tip tracking accuracy, we use Intersection over Union (IoU) (or Jaccard index) as it is considered as the most popular metric used for object detection evaluation. In the object detection scope, IoU is proposed to measure the similarity between the detection bounding box and the ground-truth bounding box as illustrated in Figure 40, and defined by the following:

( )

Figure 40 -Intersection over Union (IoU)

In our task, prediction boxes correspond to YOLO outputs and ground-truth boxes are handlabeled bounding boxes from the validation set that enclose the target object (i.e. the catheter tip) to be detected. In order to get ground-truth, we have used annotation software.

The proposed catheter tip detection method is also evaluated by comparing the automatically tracked trajectories to the manual locations and computing the mean Euclidean distance between both.

Hepatic arterial tree repositioning assessment

Arterial tree repositioning is initialized with the 3D/2D structure-driven registration method described in chapter 2, performed between the preoperative 3D CTA and the intraoperative 2D DSA acquired when the catheter tip is positioned at the level of the celiac trunk. The resulting projection position is corrected and updated in the fluoroscopic image based on the catheter tip detection. Structure repositioning is evaluated by assessing if the micro-catheter is continuously superimposed by the projected displaced structure. For evaluation, we have recourse to an Inclusion Rate (IR) calculated on micro-catheter points. The IR corresponds to the ratio of points belonging to the micro-catheter in the image which are included within the projected boundaries of the hepatic arterial tree. Selected points belonging to the micro-catheter are equidistant (distance between points = 10 pixels), an example is illustrated in

Catheter tip detection

The IoU values are calculated for the two sequences and represented in Table 6. For RE 01, the catheter tip detection has been successful in 359 out of the 409 frames (87%) (Figure 43).

IoU values vary from 0.26 to 0.99 and the mean IoU is 0.74. For RE 02, the catheter tip detection has been successful in 292 out of the 440 frames (66%) (figure 44). For this same sequence, IoU values vary from 0.38 to 0.99 and the mean IoU is 0.69. The tracking algorithm was not successful in detecting the catheter tip in some frames (13 % of the total number of frames in RE 01 and 34 % in RE 01). We mainly report these failures to the target low contrast (Figure 45). A possible solution to overcome this issue could consist in attaching a distinguishable radiopaque material to the catheter tip, so that it can be detected more easily in each frame.

Another source of error should be related to the limited annotated data used for the YOLO model training (i.e. only four sequences have been used for the training). More data would enable to better cover the variability of imaging conditions and catheter appearance. The catheter tip displacement curves shown in (Figure 46) and (Figure 47) illustrate an example of continuous detection in the first sequence RE 01. In (Figure 46), we compare the automatically tracked trajectory (blue color) to the manual locations (gold color) in the horizontal (x) direction in the image, whereas in (Figure 47) we compare the two trajectories in the vertical (y) direction.

The similarity between the automatic tracking curves and the manual tracking curves indicates the accuracy of the algorithm. YOLO detection method seems to be accurate in detecting the catheter tip in 87% of the sequence frames of RE 01. The mean Euclidean distance calculated over these frames is 0.6 mm with a maximum distance of 2.27 mm and minimum distance of 0 mm.

Hepatic arterial repositioning

We have tested repositioning on the same two X-Ray fluoroscopy sequences used for the evaluation of the catheter tip (i.e. RE 01 and RE 02). For the 138 frames of RE 01 where the micro-catheter is introduced in the hepatic arterial tree, 899 inspected points are included in the repositioned vasculature out of 999 points, corresponding to an inclusion rate (IR) of 90% (Figure 50). For the 134 frames of RE 02 where the micro-catheter is introduced in the hepatic arterial tree, 767 points are included in the repositioned vasculature out of 928 points, corresponding to an inclusion rate (IR) of 82% (Figure 51).

Conclusion

In this work, we have proposed an approach to reposition the hepatic arterial tree on 2D X-Ray fluoroscopic sequences in real-time during radioembolization procedures. Our approach allows for the continuous overlay of projected 3D preoperative hepatic structure onto live 2D fluoroscopy despite the respiratory motion. Evaluations of the overall approach were performed on two X-Ray fluoroscopic sequences of liver catheterization during radioembolization procedures, RE 01 and RE 02. The obtained results show that the framework used for the catheter tip detection showed accuracy and robustness on tracking the catheter tip in real time, despite the difficulties related to the lack of discriminating characteristics, low contrast, and low size of the catheter tip. For both cases, the CNN-based method has succeeded in detecting the catheter tip with a detection rate of 87% and 66%, a mean Intersection over Union (IoU) of 0.74 and 0.69, and a mean Euclidean distance of 0.6 mm and 0.69 mm, respectively. The hepatic arterial tree repositioning has also shown efficiency in including the micro-catheter with an IR of 90% and 82% for RE 01 and RE 02, respectively.

Our proposed approach could be applied to integrate the motion related to respiration when augmenting the intraoperative image, and thus provide better micro-catheter localization inside the hepatic vascular structure.

Our proposed approach does not require contrast medium injection / 2D angiographic images nor user interaction.

The three fusion steps were independently evaluated on real clinical data. The approach resulted in qualitatively appropriate anatomical correspondences when projecting the preoperative structures on intraoperative images. With best configurations, the registration steps showed accuracy and feasibility in aligning data, with a limited user interaction and a computation time compatible with the clinical workflow.

Abdominal organs, including the liver, change their positions continuously due to the patient's respiration. Our second line of research dealt with the issue of continuous repositioning of the preoperative hepatic vasculature on the intraoperative fluoroscopy sequence.

We have indeed proposed a CNN based approach to reposition the hepatic arterial tree on 2D X-Ray fluoroscopic sequences in real-time, in order to perform a more fluent and effective catheterization. Our approach allows for the overlay of projected 3D preoperative hepatic structure onto live 2D fluoroscopy despite the respiratory motion. As the hepatic arterial tree displacement can be described by the catheter movement, we propose to update the roadmap position based on the tracking of the 2D catheter tip visualized in intraoperative live 2D X-Ray fluoroscopic sequence. Preliminary results show that the overall approach was robust and could be applied to integrate the respiration induced motion when augmenting the intraoperative image, and thus provide better micro-catheter localization inside the hepatic vascular structure.

The work carried out during this thesis opens up different perspectives for each line of research.

Regarding the 3D/2D registration framework, although results obtained are generally satisfactory, the introduced method presents some limits which might deteriorate the registration process or hamper the catheter manipulation. An initial transformation that is distant from the gold standard increases the failure probability of the registration, resulting in inappropriate anatomical correspondences. A technique using virtual fiducial markers (VFM) could be integrated for the automation of the initial transformation definition [START_REF] Varnavas | Increasing the automation of a 2D-3D registration system[END_REF].

Moreover, although the processing time is compatible with the clinical workflow, it could still be reduced through further algorithmic optimization.

Even though bone-based registration is performed with a relatively low global mean registration error, the projection of the celiac trunk origin can be inaccurate in some cases. We For the vessel-based registration, the approach could be improved by providing an automatic solution for appropriate preoperative data segmentation to remove ambiguities present when an extra vascular segment is included to the HVS volume. For some patients, the HVS is a more likely to be deformed, which might restrict the interest of the proposed strategy for injection site access. For those cases, taking into account this deformation would permit improving the preoperative and intraoperative structures matching.

Regarding the hepatic arterial tree repositioning, our approach relies on the assumption that there is a link between the catheter tip and the hepatic arterial tree. More exhaustive experiments need to be carried out for this purpose to confirm the interest of the catheter tip detection in updating the hepatic arterial tree position.

Figure 1 - 19 Figure 2 - 20 Figure 3 -

 1192203 Figure 1 -Guidage assisté par ordinateur. Le volume CTA préopératoire est segmenté semiautomatiquement à l'aide d'endosize®, obtenant soit un volume osseux 3D, soit un volume vasculaire délimité. Au cours de l'intervention de prétraitement, le CTA est d'abord aligné avec l'image fluoroscopique temps réel suivant un recalage osseux 3D/2D (a), et l'aorte abdominale (AA) ainsi que les origines du tronc coeliaque et mésentérique supérieure sont projetés pour aider l'accès au tronc coeliaque (b). Au niveau du tronc coeliaque, un produit de contraste est injecté et une angiographie est acquise et alignée au CTA à travers un recalage vasculaire (c), et l'arbre artériel hépatique est projeté pour aider l'accès au site d'injection (d). Une fois atteint le site d'injection, une image fluoroscopique est capturée (e), et ultérieurement alignée avec l'image fluoroscopique pendant l'intervention de traitement, pour aider la reproduction du site d'injection (f, g). ....................................................................... 19

Figure 4 - 23 Figure 5 - 30 Figure 6 - 31 Figure 7 - 32 Figure 8 - 33 Figure 9 -

 4235306317328339 Figure 4 -Recalage 2D/2D et évaluation basée sur des marqueurs. La fluoroscopie de prétraitement capturée une fois que le cathéter a atteint le site d'injection (a) est alignée sur l'image fluoroscopique peropératoire de traitement (b). Ce recalage aide la reproduction du trajet du cathéter ainsi que le site d'injection (c). Les points marqués (points de couleur

Figure 10 - 39 Figure 11 - 40 Figure 12 - 41 Figure 13 - 46 Figure 14 - 49 Figure 15 -

 1039114012411346144915 Figure 10 -Radioembolization intervention overview (inspired by [29]). .............................. 39

53 Figure 17 - 54 Figure 18 - 54 Figure 19 -

 531754185419 Figure 17 -An exmple of preoperative CTA (a) segmentation. The bone structure extracted from CTA (b) corresponds to the anatomical structure visible in intraoperative fluoroscopic image (c), while the vascular structure extracted from CTA (d) corresponds to the anatomical structure visible in intraoperative angiography (e). ................................................................ 54

  is the plane parallel to the plane of intraoperative image and is aligned with the main axis of c-arm. and correspond to in-plane translations and corresponds to the out-plane translation (along the axes of ). and correspond to the out-plane rotations and corresponds to the in-plane rotation (around the axes of ) ................................................................................ 56

Figure 20 - 60 Figure 21 - 61 Figure 22 - 62 Figure 23 - 64 Figure 24 - 65 Figure 25 - 67 Figure 26 - 73 Figure 27 - 74 Figure 28 - 76 Figure 29 - 79 Figure 30 - 80 Figure 31 - 80 Figure 32 - 82 Figure 33 - 86 Figure 34 - 86 Figure 35 - 88 Figure 36 - 89 Figure 37 - 90 Figure 38 - 92 Figure 39 - 93 Figure 40 - 94 Figure 41 - 95 Figure 42 - 96 Figure 43 - 98 Figure 44 - 99 Figure 45 -

 206021612262236424652567267327742876297930803180328233863486358836893790389239934094419542964398449945 Figure 20 -Boxplot of the mean errors obtained with each SM for 20 patient cases ............. 60

  Au cours de l'intervention de prétraitement, pour faciliter l'accès au tronc coeliaque, l'aorte et les origines de ses branches latérales sont extraites du volume CTA tridimensionnel préopératoire et projetées sur la fluoroscopie bidimensionnelle peropératoire suivant une méthode de recalage osseux basé-intensité. Par la suite, pour aider à la localisation du site d'injection, une approche similaire consiste à projeter le système vasculaire hépatique du CTA tridimensionnel sur l'angiographie bidimensionnelle peropératoire suivant un recalage vasculaire basé-intensité.Enfin, pendant l'intervention de traitement, le site d'injection sélectionné lors de l'intervention de prétraitement est reproduit à travers un recalage 2D/2D des images fluoroscopiques de prétraitement et de traitement. Les trois étapes de fusion sont représentées dans (Figure1).La projection statique de la structure vasculaire hépatique sur l'angiographie peropératoire, notamment illustrée dans (Figure1 -(d)), peut ajouter des informations pertinentes et fournir une visualisation plus complète de la scène chirurgicale dans les procédures de radioembolisation. Les organes abdominaux, y compris le foie, changent continuellement leurs positions en raison de la respiration du patient. Un système de guidage précis doit prendre en considération ce déplacement par une mise à jour continue de l'alignement spatial du système vasculaire hépatique préopératoire avec la position du système vasculaire hépatique peropératoire. Dans cette thèse, notre troisième contribution principale consiste à présenter une méthode pour mettre à jour la position du système vasculaire 3D projeté en fonction de la détection de l'extrémité du cathéter visualisée en séquence fluoroscopique bidimensionnelle peropératoire. La méthode proposée est basée sur le réseau neuronal convolutif (CNN) et se déroule en temps réel.

Figure 1 -

 1 Figure 1 -Guidage assisté par ordinateur. Le volume CTA préopératoire est segmenté semiautomatiquement à l'aide d'Endosize®, obtenant soit un volume osseux 3D, soit un volume vasculaire délimité. Au cours de l'intervention de prétraitement, le CTA est d'abord aligné avec l'image fluoroscopique temps réel suivant un recalage osseux 3D/2D (a), et l'aorte abdominale (AA) ainsi que les origines du tronc coeliaque et mésentérique supérieure sont projetés pour aider l'accès au tronc coeliaque (b). Au niveau du tronc coeliaque, un produit de contraste est injecté et une angiographie est acquise et alignée au CTA à travers un recalage vasculaire (c), et l'arbre artériel hépatique est projeté pour aider l'accès au site d'injection (d). Une fois atteint le site d'injection, une image fluoroscopique est capturée (e), et ultérieurement alignée avec l'image fluoroscopique pendant l'intervention de traitement, pour aider la reproduction du site d'injection (f, g).

  Figure3).

Figure 2 -

 2 Figure 2 -Évaluation de l'accès au tronc coeliaque pour deux cas de patients différents. Le volume CTA préopératoire est aligné avec l'image fluoroscopique peropératoire au moyen d'un recalage osseux (a) (d). La projection de l'aorte abdominale et des branches latérales (b) (e) entraîne un chevauchement de l'ostium du tronc coeliaque et du cathéter dans le premier cas (c) alors qu'un léger décalage entre les deux composantes est noté dans le second cas (f).

Figure 3 -

 3 Figure 3 -L'angiographie (a) et l'arbre artériel hépatique sont alignés suivant un recalage vasculaire basé-intensité (b). Une bonne correspondance anatomique est obtenue lors de la projection de l'arbre artériel hépatique (c).

Figure 4 -

 4 Figure 4 -Recalage 2D/2D et évaluation basée sur des marqueurs. La fluoroscopie de prétraitement capturée une fois que le cathéter a atteint le site d'injection (a) est alignée sur l'image fluoroscopique peropératoire de traitement (b). Ce recalage aide la reproduction du trajet du cathéter ainsi que le site d'injection (c). Les points marqués (points de couleur rouge dans (a) et points de couleur bleue dans (b)) qui sont définis par l'expert et utilisés pour l'évaluation sont visualisés dans les images.

Figure 5 -

 5 Figure 5 -Ligamentous attachments of the liver. From https://teachmeanatomy.info/abdomen/viscera/liver/

Figure 6 -

 6 Figure 6 -Anterior and posterior surfaces of liver illustrating functional division of the liver following Couinaud's segmental classification (Inspired by [4]).

Figure 7 -

 7 Figure 7 -Blood supply of the liver (from [5]).

Figure 8 -

 8 Figure 8 -Portal vein anatomy. The portal vein is formed by the confluence of the splenic and superior mesenteric veins. The inferior mesenteric vein drains into the splenic vein. The coronary (left gastric) vein drains into the portal vein in the vicinity of the confluence. v. = vein (inspired by [4]).

34 Figure 9 -

 349 Figure 9 -Arterial anatomy of the upper abdomen and liver, including the celiac trunk and hepatic artery branches. LHA = left hepatic artery; RHA = right hepatic artery (from [4]).

Figure 10 -

 10 Figure 10 -Radioembolization intervention overview (inspired by [29]).

Figure 13 -

 13 Figure 13 -Proposed navigation system. During pretreatment intervention, the preoperative arterial phase Computed Tomography (CTA) images (a) are firstly registered to the real-time fluoroscopic image (b), and the abdominal aorta (AA) as well as the celiac trunk and the superior mesenteric origins are projected to assess the celiac trunk catheterization (c). At the level of the celiac trunk, a contrast product is injected and a DSA is acquired (d) and registered to the CTA based on a delimited vascular-structure, and the hepatic arterial tree is projected to assess the IS access (e). Once reaching the IS, a fluoroscopic image is captured (f), and registered to the real time fluoroscopic image (g) during the treatment intervention, to assess the IS reproduction (h).

Registration approach 49 Different

 49 2D/3D registration methods are introduced in the literature. Our investigated method requires speed and accuracy as two important aspects of performance in the context of RE image guidance. Our overall proposed solution is intended be integrated in EndoNaut®, THERENVA for the guidance of radioembolization interventions. EndoNaut® is an intraoperative navigation solution device provided by THERENVA and used for aortoiliac

Figure 15 -

 15 Figure 15 -EndoNaut® navigation device, THERENVA.

Figure 17 -

 17 Figure 17 -An exmple of preoperative CTA (a) segmentation. The bone structure extracted from CTA (b) corresponds to the anatomical structure visible in intraoperative fluoroscopic image (c), while the vascular structure extracted from CTA (d) corresponds to the anatomical structure visible in intraoperative angiography (e).

Figure 18 -

 18 Figure 18 -Preoperative CTA segmentation using Endosize®, sizing software, THERENVA.The segmentation process takes about 10 min.

Figure 19 -

 19 Figure 19 -Geometric configuration used for registration.is the world coordinate system, is the intraoperative image coordinate system, is the preoperative volume coordinate system and is the coordinate system where: is the plane parallel to the plane of intraoperative image and is aligned with the main axis of c-arm. and correspond to in-plane translations and corresponds to the out-plane translation (along the axes of ). and correspond to the out-plane rotations and corresponds to the inplane rotation (around the axes of )

  and randomly selected according to the data availability. Patient informed consent was obtained for being registered anonymously in the research database. Contrast-enhanced CT scan (i.e. CTA) and 2D X-Ray images (i.e. fluoroscopy) are provided within the standard imaging protocol. The contrast-enhanced CTs were acquired on a SOMATOM Definition (Siemens Medical systems, Forchheim, Germany) with the following typical parameters: 203 slices number, 0.7890 x 0.7890 mm pixel size, 1.5 mm slice thickness, 1 pitch, 19 x 1.2 mm collimation, 1085 mm source to detector distance and 137 mA X-Ray tube Current. The 2D fluoroscopic images were acquired intraoperatively on a C-arm Artis Zeego (Siemens Healthcare, Forchheim, Germany) with 1149 mm source to detector distance and 0.3729 x 0.3729 mm pixel size. These intrinsic parameters have been used for the generation of DRRs.

Figure 20 -

 20 Figure 20 -Boxplot of the mean errors obtained with each SM for 20 patient casesIn Table2, we show the results by considering the threshold T = 5 mm. Registration demonstrated a success rate of 274 registrations out of 300 using GD with a global mean error of 1.59 mm calculated on registrations achieving a mean 2D Euclidean distance below T.

Injection site access 61 appreciably

 61 help radiologists on catheter manipulation (Figure21 (c)). In the remaining 17 % of the cases, a shift between the catheter position and the ostium can eventually slightly hamper the navigation (Figure21 (f)).

Figure 21 -

 21 Figure 21 -Celiac trunk access assessment for two different patient cases. Preoperative CTAs are registered to intraoperative fluoroscopic images based on bony-structures (a) (d). The projection of AA and side branches (b) (e) results in a celiac trunk ostium and catheter overlap in the first case (c) whereas a slight shift between the two components is noticed in the second case (f).

Figure 22 -

 22 Figure 22 -Preoperative and intraoperative vascular-structure visualization for a typical patient case. To access the IS, several fluoroscopic images showing incomplete vasculature are acquired with contrast injection at different locations of the catheter tip: i1 (celiac trunk origin), i2 (proper hepatic artery) and i3 (right hepatic artery) (a). A more complete vasculature is visualized in the CTA extracted volume (b).

Figure 23 -

 23 Figure 23 -Three dimensional volume-rendering (3D VR) generated from CTA showing the hepatic arterial anatomic structures. CHA = common hepatic artery, GDA = gastroduodenal artery, PHA = proper hepatic artery, LHA = left hepatic artery and RHA = right hepatic artery

Figure 25 -

 25 Figure 25 -Influence of the SM on vessel-based registration. The DSA (a) and the HVS are correctly registered with GD (b). A good anatomical correspondence is obtained when projecting the hepatic arterial tree (c). The registration result is degraded with NCC (d).

Figure 26 -

 26 Figure 26 -Injection site reproduction in treatment intervention.

Figure 27 -

 27 Figure 27 -Injection site reproduction strategy.

Figure 30 -Figure 31 - 3 . 4

 303134 Figure 30 -Marked-based evaluation. Pretreatment assessment fluoroscopy captured once the catheter reached the injection site (a) is registered to the intraoperative real-time fluoroscopic image (b) during the treatment intervention. The registration results in a reproduction of the catheter path as well as the IS (c). Marked points (red colored points in (a) and blue colored points in (b)) which are defined by expert are highlighted in the images

Figure 32

 32 Figure 32 gives an overview of the different fusion steps involved in RE pretreatment assessment intervention (celiac trunk access, injection site access) and treatment intervention (injection site reproduction). Although for the treatment intervention guidance we have focused on the reproduction of the injection site, the celiac trunk access can be assisted through the representation of the pretreatment catheter path. It could be also assisted by the

Figure 32 -

 32 Figure 32 -Overview of proposed navigation approach. Preoperative arterial phase Computed Tomography (CTA) images are semi-automatically segmented using endosize®, obtaining either a 3D bony or delimited vascular volume. During pre-treatment intervention, the CTA is firstly registered to the real-time fluoroscopic image based on the bony-structure (a), and the abdominal aorta (AA) as well as the celiac trunk and the superior mesenteric origins are projected to assess the celiac trunk catheterization (b). At the level of the celiac trunk, a contrast product is injected and a DSA is acquired and registered to the CTA based on a delimited vascular-structure (c), and the hepatic arterial tree is projected to assess the IS access (d). Once reaching the IS, a fluoroscopic image is captured (e), and registered to the real time fluoroscopic image during the treatment intervention, to assess the IS reproduction (f, g).

Figure 33 -

 33 Figure 33 -Global overview: vessels/catheter extraction and 2D/3D registration. From [29].

Figure 34 -

 34 Figure 34 -roadmap correction for hepatic artery catheterizations. (a) displays the 3D static roadmap of the hepatic arteries. (b) shows a 2D fluoroscopy image. (c) illustrates the projection of 3D static roadmap onto a frame in the 2D fluoroscopy image sequence without motion correction. (d) indicates the projection of 3D static roadmap onto the frame with motion correction. From [62].

  Rohlfing et al.[START_REF] Rohlfing | Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images[END_REF] inspected the transformation and deformation occurred during the respiration by performing rigid and non-rigid registration. They indicate that the dominant component of liver motion was cranio-caudal translation (12 to 26 mm). The same group reported that Left-Right translation was between 1 to 3 mm and the rotation did not exceed 1.5 degrees. By comparing the rigid and non-rigid registration, the same group estimated an average tissue deformation of 6 mm occurred in the liver periphery, while the rigid motion model accurately aligned the central part of the liver. Clifford et al.[START_REF] Clifford | Assessment of hepatic motion secondary to respiration for computer assisted interventions[END_REF] provided an extensive assessment of the hepatic motion caused by respiration based on nine published studies. All mentioned studies agree that the most significant liver motion is cranio-caudal translation, with a translation ranging from 10 to 26 mm in quiet respiration. There exists a disagreement in the literature about the significance of Anterior-Posterior and Lateral translations. Although some studies indicate that the respiration causes a significant translation along the two axis,

Figure 35 -

 35 Figure 35 -Patient and C-arm positioning during radioembolization procedure. From [98].

Figure 37 -

 37 Figure 37 -X-Ray fluoroscopy visualizing the guiding catheter in radioembolization

Figure 38 -

 38 Figure 38 -R-CNN, Fast-RCNN and Faster-RCNN architectures [106]-[108].

Figure 39 -

 39 Figure 39 -Object detection using YOLO [109].

Figure 41 - 4 Results 4 . 4 . 1

 414441 Figure 41 -Inclusion Rate (IR) calculation in a single frame:micro-catheter used points

Figure 42 -

 42 Figure 42 -image annotation using labelimg.

Figure 43 -

 43 Figure 43 -IoU on different frames of sequence RE 01. Blue bounding boxes correspond to the ground truth whereas red bounding boxes correspond to the tracking output.

Figure 45 -

 45 Figure 45 -examples of non-detected catheter tip in RE 01 (a) and RE 02 (b)

Figure 46 -Figure 47 -

 4647 Figure 46 -Horizontal displacement of the catheter tip in sequence RE 01: « Frames » axis presents the frame index in which the catheter tip is detected and -pixels‖ axis presents the catheter tip horizontal position

Figure 48 -Figure 49 -

 4849 Figure 48 -Horizontal displacement of the catheter tip in sequence RE 02: « Frames » axis presents the frame index in which the catheter tip is detected and -pixels‖ axis presents the catheter tip horizontal position

Figure 50 -Figure 51 -

 5051 Figure 50 -Hepatic vasculature repositioning result on different frames of sequence RE 01

  noticed occasionally a slight misalignment between the projected celiac trunk and the inserted catheter observed in the fluoroscopic image. It can be explained by the patient repositioning resulting in a vasculature shift. This resulting projection error has the potential to decrease the precision of the catheter tip localization. A possible solution might consist in estimating an average misalignment range of the celiac trunk origin by calculating, for several patients, the distance between the projected ostium (belonging to the abdominal aorta) and the 2D catheter visualized in the 2D fluoroscopic image. The obtained margin could then be used to better highlight the area of the celiac trunk origin where the catheter should be inserted.

  Figure 49 -Vertical displacement of the catheter tip in sequence RE 02: « Frames » axis presents the frame index in which the catheter tip is detected and -pixels‖ axis presents the catheter tip vertical position .................................................................................................. 103

	Figure 50 -Hepatic vasculature repositioning result on different frames of sequence RE 01

Figure 46 -Horizontal displacement of the catheter tip in sequence RE 01: « Frames » axis presents the frame index in which the catheter tip is detected and -pixels‖ axis presents the catheter tip horizontal position .............................................................................................. 101 Figure 47 -Vertical displacement of the catheter tip in sequence RE 01: : « Frames » axis presents the frame index in which the catheter tip is detected and -pixels‖ axis presents the catheter tip vertical position .................................................................................................. 101 Figure 48 -Horizontal displacement of the catheter tip in sequence RE 02: « Frames » axis presents the frame index in which the catheter tip is detected and -pixels‖ axis presents the catheter tip horizontal position .............................................................................................. 102 ................................................................................................................................................ 104 Figure 51 -Hepatic vasculature repositioning result on different frames of sequence RE 02 ................................................................................................................................................ 105

Table 1 -

 1 Parameters used during the optimization phases (r = resolution of the discretized range of the parameters) .......................................................................................................... 57

Table 2 -

 2 Bone-based registration results using different similarity measures. Global mean error is calculated on registrations obtaining a mean 2D Euclidean distance < T ................ 60

Table 3 -

 3 Parameters used during the optimization phases (r = resolution of the discretized

range of the parameters) .......................................................................................................... 63

Table 4 -Vessel-based registration results using different similarity measures. Global mean error is calculated on registrations obtaining a mean 2D Euclidean distance < T ................ 66

Table 5 -

 5

		LIST OF ABBREVIATIONS
	RE	Radioembolization
	99m Tc-MAA 99mTc macro aggregated albumin
	Y90	Yttrium-90
	166Ho	Holmium-166
	DSA	Digitally Subtracted Angiography
	CTA	Computer Tomography Angiography
	HVS	hepatic vascular-structure

Parameters used during the optimization phases (r = resolution of the discretized range of the parameters) .......................................................................................................... 77 Table 6 -catheter tip detection results ..................................................................................... 97

  ). Afin d'optimiser le traitement, la procédure est réalisée en deux séances : intervention de prétraitement et intervention de traitement. L'intervention de prétraitement présente une simulation préalable de traitement. Elle est principalement réalisée pour :

localiser le site d'injection, évaluer la distribution intrahépatique des microsphères 90Y/166Ho, détecter le shunt extrahépatique et réaliser une évaluation dosimétrique. L'intervention de traitement est effectuée une à deux semaines après l'intervention de prétraitement pour la délivrance d'une dose appropriée de microsphères 90Y/166Ho dans le site d'injection préalablement localisé. Lors des deux interventions, les radiologues interventionnels manipulent soigneusement le cathéter sous un guidage fluoroscopique. Les images fluoroscopiques bidimensionnelles utilisées visualisent des informations de base sur l'anatomie du patient (essentiellement la structure osseuse) et les outils d'intervention insérés. Cependant, la visualisation de la structure vasculaire non permanente nécessite l'acquisition répétitive d'angiographies bidimensionnelles réalisées par injection de produit de contraste, dont une dose excessive peut être néfaste pour le patient. En raison de la complexité du système vasculaire hépatique, la manipulation du cathéter reste une tâche difficile pour les radiologues. Dans cette thèse, nous proposons une stratégie de guidage assistée par ordinateur conçue pour augmenter progressivement l'image fluoroscopique peropératoire avec les informations appropriées. Cette approche est en mesure d'assurer une navigation fiable du cathéter et a le potentiel de réduire non seulement l'administration de produits de contraste, mais aussi le l'exposition aux rayonnements et le temps d'intervention.

Notre approche est conçue pour guider l'intervention de prétraitement et l'intervention de traitement, présentant nos deux premières contributions principales.

• Première contribution: Guidage de l'intervention de prétraitement

  La projection de l'aorte et de la structure vasculaire hépatique, semble être précise pour manipuler le cathéter et, par conséquent, a le potentiel de réduire l'administration de produit de contraste et l'exposition aux rayonnements (Figure2 et

	Résumé long en français
	Les deux étapes de fusion conçues pour guider l'intervention de prétraitement par l'aide à
	l'accès au tronc coeliaque et au site d'injection ont été évaluées indépendamment sur des sous-
	ensembles de 20 et 19 patients, respectivement. L'approche a abouti à des correspondances
	anatomiques qualitativement appropriées lors de la projection des structures préopératoires
	sur les images peropératoires. Avec la meilleure configuration, les étapes de recalage ont
	montré la précision et la faisabilité de l'alignement des données, avec des erreurs moyennes
	globales de 1,59 mm et 2,32 mm, respectivement, un temps de calcul qui n'a jamais dépassé 5
	s et 25 s, respectivement, et une interaction utilisateur limitée à l'initialisation manuelle du
	recalage 3D/2D.
	L'approche proposée repose sur une méthode de recalage 3D/2D basée intensité, guidée par
	les structures anatomiques d'intérêt (DRR générée à partir d'un prétraitement de l'image 3D
	préopératoire) et mettant en oeuvre une recherche exhaustive multi résolution. Dans ce
	contexte, l'exactitude et la précision du recalage (osseux et vasculaire) dépendent de la
	sélection de la mesure de similarité. Nos expériences ont permis de montrer qu'une mesure de
	similarité basée sur la différence de gradient est la plus appropriée.
	20

• Deuxième contribution : Guidage de l'intervention de traitement

	Concernant l'accès au site d'injection par projection d'arbres hépatiques (Figure 3), afin de 2,17 mm, un temps de calcul qui n'a jamais dépassé 11 s, de manière complètement
	réaliser correctement la méthode de recalage, il a été remarqué, dans notre étude, qu'il est automatique (sans aucune interaction avec l'utilisateur).
	nécessaire de privilégier les structures préopératoires rigides ainsi que les structures qui
	apparaissent sur l'image peropératoire. Dans ce contexte, l'approche pourrait être améliorée Dans la plupart des cas, le modèle de transformation rigide 2D/2D s'est avéré suffisant pour le
	par une méthode automatique permettant de segmenter uniquement les structures 3D recalage d'images fluoroscopiques de prétraitement et de traitement. La projection du cathéter,
	préopératoires visibles sur l'image peropératoire (structures vasculaires injectées). semble être précise pour faciliter la manipulation du cathéter et reproduire le site d'injection
	et donc, réduire l'administration de produit de contraste, l'exposition aux rayonnements et le
	risque de dépôt inadéquat de 90Y/166Ho.
	Reproduire le site d'injection prédéfini avec précision lors de l'intervention de prétraitement
	est encore une tâche difficile pour les radiologues. En effet, afin d'éviter les écarts de
	distribution de dose, le site d'injection doit être reproduit le plus fidèlement possible.
	Jusqu'à présent, lors de l'intervention de traitement, les radiologues se réfèrent à leur capacité
	à comprendre le système vasculaire, à manipuler efficacement le cathéter à l'intérieur et à
	localiser mentalement le site d'injection. Une mauvaise perception peropératoire des
	structures anatomiques et une reproduction inappropriée de la position du site d'injection
	peuvent certainement entraîner des écarts de distribution de dose 90Y/166Ho.
	La reproduction automatique du site d'injection n'ayant pas été étudiée dans la littérature,
	) est effectué avec une erreur de recalage moyenne dans cette thèse, nous avons étendu notre approche de guidage pour proposer une
	globale relativement faible, la projection de l'origine du tronc coeliaque peut aussi être reproduction automatique de ce site en augmentant l'image fluoroscopique 2D peropératoire
	inexacte dans certains cas. Nous avons remarqué occasionnellement un léger désalignement du traitement avec le trajet du cathéter extrait de la fluoroscopie de prétraitement au moyen
	entre le tronc coeliaque projeté et le cathéter inséré observé sur l'image fluoroscopique. Cela d'un recalage 2D/2D (Figure 4). La visualisation du trajet du cathéter pourrait simultanément
	peut s'expliquer par le repositionnement du patient entraînant un décalage vasculaire. Cette aider à spécifier la position d'origine du tronc coeliaque et l'emplacement du site d'injection.
	erreur de projection résultante a le potentiel de diminuer la précision de la localisation de la Ce recalage 2D/2D de l'image fluoroscopique de prétraitement avec l'image fluoroscopique pointe du cathéter. Une solution possible pourrait consister à estimer une plage moyenne de de traitement a été évalué sur un sous-ensemble de 5 patients. L'approche a montré la désalignement de l'origine du tronc coeliaque en calculant, pour plusieurs patients, la distance entre l'ostium projeté (appartenant à l'aorte abdominale) et le cathéter 2D visualisé sur l'image précision et la faisabilité de l'alignement des données, avec une erreur moyenne globale de
	fluoroscopique 2D. La marge obtenue pourrait alors être utilisée pour mieux mettre en
	évidence la zone d'origine du tronc coeliaque où le cathéter doit être inséré.

• Troisième contribution : Repositionnement de la structure hépatique projetée

	A ce niveau nous abordons le problème du suivi dynamique de la structure vasculaire sur
	l'image fluoroscopique augmentée. Afin de repositionner l'arbre artériel hépatique sur la
	séquence fluoroscopique, la structure vasculaire hépatique préopératoire est tout d'abord
	alignée avec l'angiographie peropératoire selon la méthode de recalage vasculaire 3D/2D
	présentée. La structure vasculaire 3D est ensuite projetée sur la séquence fluoroscopique et sa
	position dans le plan est mise à jour en fonction de l'emplacement 2D de l'extrémité du
	cathéter. Celle-ci est localisée dans chaque trame de la séquence en utilisant une approche de
	suivi basée sur un réseau de neurones convolutif (CNN).
	Le CNN a été entrainé sur quatre séquences fluoroscopiques et la méthode de suivi proposée a
	été évaluée sur deux séquences fluoroscopiques réelles de deux patients différents. Dans les
	deux cas, la méthode de suivi a montré une précision et faisabilité de la détection de
	l'extrémité du cathéter en temps réel, avec un taux de détection de 87 % et 66 %, un indice de
	recouvrement de 0,74 et 0,69, et une erreur moyenne de 0,6 mm et 0,69 mm, respectivement.
	Le repositionnement de l'arbre hépatique a abouti à des correspondances anatomiques
	appropriées démontrées par un taux d'inclusion rapporté à 90 % et 82 %, respectivement.
	L'approche globale ne nécessite pas d'interaction manuelle de l'utilisateur.
	Ces résultats montrent que la méthode utilisée pour le suivi de l'extrémité du cathéter est
	robuste, malgré les difficultés liées au manque des caractéristiques discriminantes, le faible
	contraste et la faible taille de l'extrémité du cathéter. Le repositionnement de l'arbre artériel
	hépatique a également montré son efficacité. L'approche proposée ne nécessite pas d'injection
	de produit de contraste / d'images angiographiques 2D ni d'interaction avec l'utilisateur. Elle
	permet d'intégrer le mouvement lié à la respiration lors de l'augmentation de l'image
	fluoroscopique peropératoire, et ainsi de faciliter le cathétérisme de l'arbre artériel hépatique
	lors des procédures de radioembolisation.

Table 2 -

 2 Bone-based registration results using different similarity measures. Global mean error is calculated on registrations obtaining a mean 2D Euclidean distance < T

	Similarity measure GI	GD	GC	NCC	MI
	Success rate	214/300	274/300 125/300	129/300	76/300
	Global mean error	1.88	1.59	2.32	2.34	3.03

Table 4 -

 4 Vessel-based registration results using different similarity measures. Global mean error is calculated on registrations obtaining a mean 2D Euclidean distance < T

	Similarity measure	GI	GD	GC	NCC	MI
	Success rate	138/285	239/285	99/285	27/285	45/285
	Global mean error	2.94	2.32	3.34	3.53	3.46
	The best results are obtained when using the GD with a success rate of 239 out of 285
	registrations and a global mean error of 2.32 mm calculated on registrations achieving a mean
	2D Euclidean distance below T, while NCC failed on achieving a significant success rate (27
	out of 285 registrations). (Figure

Table 6 -

 6 catheter tip detection results

	Sequence	Frames	Detection	IoU min	IoU max	IoU mean
			rate
	RE 01	409	359 (87%)	0.26	0.99	0.74
	RE 02	440	292 (66%)	0.38	0.99	0.69

Pretreatment assessment intervention guidance

Treatment intervention guidance: injection site reproduction

Figure 44 -IoU on different frames of sequence RE 02: Blue bounding boxes correspond to the ground truth whereas red bounding boxes correspond to the tracking output.

the celiac trunk) undergo only a slight caudal motion of 1.3±1.1 mm. Another study that was performed by Ali R. [START_REF] Raheem | Non-rigid 2 D-3 D image registration for use in Endovascular repair of Abdominal Aortic Aneurysms[END_REF] et al. asserts that the main cause of the abdominal aorta (AA) displacement or deformation is the introduction of stiff instruments. As catheters used in the context of RE are flexible, we assume that they don't able to deform the vasculature.

Given these works and assumptions, and knowing that the celiac trunk catheterization is mainly performed under the guidance of 2D fluoroscopic images where only the inserted catheter as well as the bone structure are visible, we choose to segment and only retain the bone structure in preoperative 3D CTA to perform a rigid 3D/2D registration between preoperative CTA and intraoperative 2D fluoroscopy. Indeed, we have investigated the accuracy of the registration method that is described above on augmenting the 2D real-time fluoroscopic by projecting the AA and the celiac trunk origin.

Experiments

A preliminary step consisting in extracting the bony-structure from the CTA is foremost performed in order to generate appropriate DRRs. The vascular-structure to project is also segmented, simultaneously, using Endosize®. This software allows placing three reference points in the CTA volume in order to determine the structure to extract and from where the segmentation is automatically carried out. Before assessing the registration method accuracy, preliminary tests were performed to set the exhaustive search parameters (Table 1).

Experiments

We have firstly adapted the parameter settings of the multiresolution exhaustive search (Table 3) while taking into consideration the manual initialization of the structure and the hepatic arterial tree motion. Registration is performed by considering a specific preoperative vascular-structure. Two configurations were tested by considering: 1) hepatic vascular-structure (HVS) including:

CHA, PHA, the departure of the gastroduodenal artery (GDA), LHA and RHA, and 2) HVS along with the splenic artery (HVSS) (Figure 23). Besides, we have investigated, as described in (subsection 2.2.1), the effect of the different similarity measures. Similarly to the bonebased registration, the method has been evaluated by comparing the estimated rigid transformation to the GS resulting one. The accuracy is expressed by the global mean error and the mean error. For vessel-based registration, the mean error represents the mean 2D

Euclidean distance computed from all the points belonging to the vascular structure obtained by applying the reference transformation and their homologues obtained by applying the resulting transformation.

( )

A similarity measure is computed between the 2D pretreatment and treatment fluoroscopic images for each combination of the parameters. The optimal transformation corresponds to the combination of parameters giving the best similarity measure outcome. It is used as the initial solution for the next resolution level.

Experiments

2D fluoroscopic images contrast was enhanced primarily by applying a histogram equalization preprocessing. There was no manual initialization for the 2D/2D registration.

The parameters of the initial transformation were fixed to 0.

Before assessing the registration method accuracy, preliminary tests were performed to set the exhaustive search parameters (Table 5).

Table 5 -Parameters used during the optimization phases (r = resolution of the discretized range of the parameters)

Step In general, distance between corresponding points in the two registered images is a preferred measure of 2D rigid registration evaluation as it is considered as one of the most reliable ways of quantifying accuracy. Used points can be anatomical landmarks or tailor-made markers. As markers are not available in our context, we have recourse to anatomical landmarks-based analysis for evaluation. Consequently, we have asked experts to manually define eight corresponding points in each image pair to be registered with accuracy. All points belong to the spine structure, which is visible in both images.

In this context, we have evaluated the 2D registration accuracy while investigating the effect of different similarity measures [START_REF] Penney | A comparison of similarity measures for use in 2D-3D medical image registration[END_REF] on the registration outcome. As for chapter 2 studies, Gradient information (GI), gradient difference (GD), gradient correlation (GC), normalized cross correlation (NCC) and Mutual information (MI) have been tested.

Conclusion and perspectives

The work presented in this thesis focused on the augmentation of intraoperative 2D X-Ray images during transarterial radioembolization for the treatment of liver cancer. Even if this treatment is already established, the complexity of this type of procedure associated with the limits of interventional imaging, still pose difficulties, in particular for the hepatic arterial tree catheterization.

In the intraoperative phase, regardless the sophistication of imaging devices, notably the Carm, radiologists used to perform transarterial catheterization with the assistance of twodimensional X-Ray images. This currently used intraoperative imaging modality provides basic information about the patient's anatomy (i.e. bone structure) and inserted interventional tools (i.e. catheter and micro-catheter). However the nonpermanent vascular structure visualization is still dependent on contrast medium injection and angiographic images acquisition, resulting in a contrast product whose excessive injection may cause severe reactions for the patient. To our knowledge, no computer-aided image guidance solutions dedicated to the RE interventional guidance and its specific issues have been introduced in the literature so far.

Our goal was to provide a radioembolization assistance solution in order to increase the precision and reliability of the procedure and minimize the contrast medium use and radiation exposure. In particular, our approach provides a more comprehensive feedback of the surgical scene by augmenting intraoperative 2D X-Ray images (i.e. fluoroscopic and angiographic images) with preoperative 3D CTA images or pretreatment assessment 2D X-Ray images, medical imaging modalities acquired as part of the clinical routine.

A first line of research focused on proposing a multi-modality image registration strategy for radioembolization pretreatment assessment and treatment sessions. Indeed, we have investigated the performance of a structure-driven intensity-based rigid registration method, which could be used in three main phases of the interventional procedure: 1) celiac trunk access 2) Injection site access and 3) reproducing the injection site during the treatment session.

Published Papers

The following papers have been published as part of this thesis: In order to optimize radioembolization outcomes, the procedure is carried out in two sessions: pretreatment assessment intervention, mainly performed to locate the injection site, assess microspheres distribution and perform dosimetry evaluation, and treatment intervention performed to inject the estimated proper dose of radioactive microspheres in the located injection site. Due to the hepatic vasculature complexity, interventional radiologists carefully manipulate the catheter, during the two interventions, under X-Ray image guidance and resort to contrast media injection in order to highlight vessels. In this thesis, we propose a novel guidance strategy that promises a simplification and accuracy of the catheter navigation during the pretreatment assessment, as well as during the treatment interventions. The proposed navigation system processes pre-and intraoperative images to achieve intraoperative image fusion through a rigid registration technique. This approach is designed to 1) assist the celiac trunk access, 2) assist the injection site access and 3) automatically reproduce the injection site during the proper intervention. Knowing that the liver undergoes a motion induced by the breathing, we also propose an approach that allows obtaining a dynamic overlay of the projected 3D vessels onto fluoroscopy.