L Mauger

G Omarjee

Mahé

M Charasson

C Le Brun

E Rossignol

L Omarjee

G Mahe

Atherosclerosis knowledge of general practice residents Diagnosis and management in primary care [Connaissance de la pathologie athéromateuse par les internes de médecine générale diagnostic

Keywords: Atherosclerosis, General practice residents, Intermittent claudication, Stable angina, Transient ischemic attack Pathologie athéromateuse, Interne de Médecine Générale, Claudication intermittente, Angor stable, Accident ischémique transitoire

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Cardiovascular diseases represent the leading cause of death worldwide. In a previous survey, we have shown that the management of patients with atherosclerosis by general practitioners depends on the locations of the disease. The aim of this survey was to assess general practice residents' (GPR) knowledge on three clinical presentations which are ischemic stroke, coronary artery disease (CAD), and peripheral artery disease (PAD).

Materials and methods:

Between May 2017 and September 2017, a national self-administered survey that we previously used to assess the GPs' knowledge was emailed to GPRs from French medicine faculties. The questionnaire was composed of three clinical cases dealing with transient ischemic attack (TIA), stable angina (SA) and intermittent claudication (IC) . Each clinical case was explored by seven similar questions. The primary endpoint was the number of GPRs who correctly answered 5 of the 7 questions for each clinical case.

Results

Five hundred and fifty-three GPRs (10%) answered the questionnaire entirely. There is a significant difference between TIA knowledge (19.9% of correct answers) which is greater than SA knowledge (0.9%) and IC knowledge (0.4%). The diagnosis was correctly done by 525 (94.9%) GPRs for TIA, 513 (92.8%) for SA, and 532 (96.2%) for IC. The main difficulties encountered by the GPRs concerned complementary investigations and treatment.

Conclusion

As for general practitioners, this study reveals a difference in GPRs' knowledge depending on the location of the atherosclerosis. Considering the results, the improvement of initial training and CME in general medicine would be desirable.

Résumé

But de l'étude

Les maladies cardiovasculaires représentent la première cause de décès dans le monde. Nous avons montré lors d'une précédente étude que la prise en charge de la pathologie athéromateuse variait significativement en fonction de la localisation de l'atteinte. Le but de cette étude est d'évaluer à travers trois cas cliniques, la connaissance des internes de médecine générale (IMG) français sur la maladie athéromateuse.

Matériel et méthodes

Entre

Conclusion

Comme pour les médecins généralistes, cette étude met en évidence une différence de connaissance de la pathologie athéromateuse en fonction de la localisation de l'atteinte. Une amélioration de la formation initiale serait donc souhaitable.

Introduction

With 15.2 million deaths in 2016, cardiovascular diseases represent the leading cause of death worldwide. The annual estimated cost of cardiovascular disease in Europe is 169 billion euros [START_REF] Leal | Economic burden of cardiovascular diseases in the enlarged European Union[END_REF]. Depending on the location of the atherosclerosis, there are different clinical presentations, such as ischemic cerebrovascular disease (ICD), coronary heart disease (CHD), and peripheral artery disease (PAD).

In a previous survey, we have shown that the management by General Practitioners (GPs) of the patients depends on the location of atherosclerosis disease. Indeed, GPs who are the first caregivers that see patients when they have symptoms have a significantly better knowledge on ICD than CHD and PAD [START_REF] Druais | La place et le rôle de la médecine générale dans le système de santé[END_REF][START_REF] Charasson | Atherosclerosis knowledge -diagnosis and management in primary care[END_REF]. The knowledge of general practice residents (GPR) is unknown.

Therefore, our objective was to assess GPR's knowledge about ICD, CHD and PAD diagnosis and management in France.

Material and methods

Between May 2017 and September 2017, a self-administered survey (Annexe 1) that we previously used to assess GPs' knowledge was emailed to GPRs from French medicine faculties. Sixteen of the 26 faculties agreed to send the questionnaire to the GPRs.

The survey was created under limesurvey® (LimeSurvey Project Team / Carsten Schmitz The survey was divided into three parts: the first part collected GPRs' characteristics, the second part comprised of three clinical cases (one for TIA, one for SA and one for IC) each with seven similar questions and the last part assessed the GPRs' training. The clinical cases section was based on previously published studies that used the same methodology to assess GP awareness on a disease [START_REF] Streit | Awareness of Stroke Risk after TIA in Swiss General Practitioners and Hospital Physicians[END_REF]. For each clinical case, we had a similar number of questions about diagnosis, definition, care network, supplementary investigations, treatment, LDL cholesterol threshold objectives and the risk for a patient to have a multiple vessels disease.

Correct answers were determined according to the latest French recommendations (According the Haute Autorité de Santé [HAS]) and were validated by specialist physicians [START_REF] Has | Principales dyslipidémies : stratégies de prise en charge[END_REF][START_REF] Has | Artériopathie des membres inférieurs[END_REF][START_REF] Has | Maladie coronarienne stable[END_REF].

The primary endpoint was the number of GPRs who correctly answered the 5 questions dealing with management for each clinical case. Statistical analyses were performed using SAS software, Version 9.4 (SAS Institute Inc., Cary, NC, USA). Categorical variables were expressed in numbers of GPRs and percentages. Comparisons across the three clinical cases were performed using logistic regression models for repeat measures followed by Tukey multiple comparison tests. A p value <0.05 was considered as statistically significant.

Statistical analyses were performed by the CIC 1414 team.

Results

About 5,500 GPRs received our survey. Five hundred and fifty-three GPRs (10%) answered the questionnaire entirely. Three hundred and ninety (70.5%) were women.

There is a significant difference between TIA knowledge (19.9% of correct answers) and both SA knowledge (0.9%) and IC knowledge (0.4%). However, we did not find any significant difference between SA and IC.

The diagnosis was correctly done by 525 (94.9%) GPRs for TIA, 513 (92.8%) for SA, and 532 (96.2%) for IC (Figure 1). The summary of the different correct answers on each clinical case concerning the diagnosis, definition, care network, supplementary investigations, treatment, LDL cholesterol threshold objectives and the risk for a patient to have a multiple vessels disease are presented in table 1.

In a case of TIA, 443 GPRs (80.1%) would hospitalize the patients to carry out further investigations. Concerning the SA case, 106 GPRs (19.2%) would prescribe the right investigations in line with the HAS recommendations. Fifty-one GPRs (9.2%) would prescribe Doppler ultrasound, treadmill and blood analysis as recommended in IC.

For the TIA case, 371 GPRs (67.1%) would prescribe antiplatelet and statin medication as recommended. Concerning the SA case, the HAS recommended therapy with antiplatelet, statin and beta-blocker medication is prescribed by only 11.4% GPRs (n=63). For the IC case, only 118 GPRs (21.3%) would prescribe the right therapy. Indeed 411 GPRs (74.3%) forget to add an ACE inhibitor.

Few GPRs knew the latest objectives of LDL, only 24.1% (n=133) answered 0.7g/dL. The HAS recommendations are not very well known. Only 24% of GPRs declare to read them for TIA, with 20.7% for SA and 8.4% for IC.

GPRs considered themselves to be less well trained about IC (71.5%).

In terms of type of CME, GPRs prefer to train on internet or via meeting with specialists.

Discussion

This study shows that there are differences in GPRs' awareness and management of atherosclerotic disease depending on its location. The best awareness was for TIA such as in our previous GPs' study [START_REF] Charasson | Atherosclerosis knowledge -diagnosis and management in primary care[END_REF]. Unlike the study about GPs, here, we don't find any significant difference between SA and IC.

As we found the same trends in the two studies concerning GPs' and GPRs' knowledge of atherosclerosis disease, this questions the quality of initial training in French medical Faculties [START_REF] Charasson | Atherosclerosis knowledge -diagnosis and management in primary care[END_REF]. TIA was the best-known disease in our studies (19.9% for GPRs and 48.2% for GPs), followed by SA (0.9% for GPRs and 3.0% for GPs) and IC (0.4% for GPRs and GPs).

It doesn't seem incoherent that IC is less known than TIA and SA. There are only a few vascular departments in France as compared to stroke and cardiology units. The development of a dedicated vascular team where medical students could practice their medical training is required [START_REF] Kolte | Vascular Teams in Peripheral Vascular Disease[END_REF]. Furthermore, it has been shown that in France the medical training is not optimal to teach the ankle brachial index measurement that is a major issue to diagnose PAD [START_REF] Mahé | Ankle-brachial index measurement: Methods of teaching in French medical schools and review of literature[END_REF]. Indeed, practical training is required [START_REF] Chaudru | Training to Perform Ankle-Brachial Index: Systematic Review and Perspectives to Improve Teaching and Learning[END_REF][START_REF] Donnou | How to become proficient in performance of the resting ankle-brachial index: Results of the first randomized controlled trial[END_REF][START_REF] Donnou | Medical students' proficiency in performance of the resting ankle-brachial index is not sustained at 6 months[END_REF]. Moreover, another explanation could be that vascular medicine has only become a specialty in 2017. We can suggest that the birth of vascular medicine as a specialty and more practical teaching might improve management gradually with better training of GPRs on IC.

Even if the management of the different diseases is not optimal, GPRs and GPs establish the correct diagnosis almost unanimously [START_REF] Charasson | Atherosclerosis knowledge -diagnosis and management in primary care[END_REF]. GPs are the first caregivers for the patient [START_REF] Druais | La place et le rôle de la médecine générale dans le système de santé[END_REF]. To correctly diagnose atherosclerotic disease allows a correct orientation of the patient and a management of the pathology by specialists potentially more aware of the recommendations.

Less than half of the GPRs and GPs had appropriate knowledge about the different atherosclerosis locations [START_REF] Charasson | Atherosclerosis knowledge -diagnosis and management in primary care[END_REF]. The main difficulties are based on supplementary investigations and treatment. Supplementary investigations are an issue in general practice especially in SA (19.2% of correct answers for GPRs and 11.6% for GPs). GPRs and GPs would perform more investigations (Troponins, NT-proBNP, HbA1c) than required by the HAS recommendations which leads to an increased cost for the public health system [START_REF] Has | Maladie coronarienne stable[END_REF]. Concerning IC, only a few GPRs (12.7%) and GPs (6.4%) prescribe a treadmill test to assess maximum walking distance. French recommendations are less precise than the American ones concerning the realization of this exam [START_REF] Has | Artériopathie des membres inférieurs[END_REF][START_REF] Gornik | AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines[END_REF].

Previous studies have shown that PAD patients do not have optimal treatment when compared with other atherosclerosis localizations [START_REF] Mcdermott | Knowledge and attitudes regarding cardiovascular disease risk and prevention in patients with coronary or peripheral arterial disease[END_REF][START_REF] Blacher | Peripheral arterial disease versus other localizations of vascular disease: the ATTEST study[END_REF]. The optimal treatment of TIA is the best known both for GPRs (67.1%) and for GPs (72.6%). HAS recommends an antiplatelet agent and a statin for the optimal treatment of TIA, which is relatively clear. For GPRs, the worst known treatment is the SA treatment (11.4%). Indeed, 49% of GPRs would prescribe ACE inhibitors for SA and 49.4% would forget to prescribe a beta-blocker. According to HAS recommendations, ACE inhibitors are recommended in case of heart failure, abnormal ventricular ejection fraction, hypertension or diabetes and this was not the case in our clinical case [START_REF] Has | Maladie coronarienne stable[END_REF]. Betablockers have a cardioprotective effect in SA [START_REF] Bunch | Effect of beta-blocker therapy on mortality rates and future myocardial infarction rates in patients with coronary artery disease but no history of myocardial infarction or congestive heart failure[END_REF].For GPs, the worse known treatment was the IC treatment (13.6%). Indeed, studies show that patients suffering from PAD had poorer cardiovascular risk management [START_REF] Gasse | Secondary medical prevention among Danish patients hospitalised with either peripheral arterial disease or myocardial infarction[END_REF][START_REF] González-Clemente | Cardiovascular risk factor management is poorer in diabetic patients with undiagnosed peripheral arterial disease than in those with known coronary heart disease or cerebrovascular disease. Results of a nationwide study in tertiary diabetes centres[END_REF]. A minority of GPs would initiate ACE inhibitors which are recommended for IC patients whatever their blood pressure according to the HOPE study [START_REF] Armstrong | Angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use is associated with reduced major adverse cardiovascular events among patients with critical limb ischemia[END_REF]. The difficulty in setting up the treatment may also stem from the fact that the recommendations are not all concordant [START_REF] Kithcart | ACC/AHA Versus ESC Guidelines for Diagnosis and Management of Peripheral Artery Disease: JACC Guideline Comparison[END_REF]. The latest ESC recommendation for PAD, advise to initiate ACE treatment only in case of hypertension (>140/80mmHg) whereas the AHA guidelines stated that the use of angiotensin-converting enzyme inhibitors or angiotensin-receptors blockers can be effective to reduce the risk of cardio-vascular ischemic events in patients with PAD [START_REF] Gornik | AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines[END_REF][START_REF] Aboyans | carotid and vertebral, mesenteric, renal, upper and lower extremity arteriesEndorsed by: the European Stroke Organization (ESO)The Task Force for the Diagnosis and Treatment of Peripheral Arterial Diseases of[END_REF].

Only few GPRs and GPs were aware of the HAS French Recommendations [START_REF] Has | Principales dyslipidémies : stratégies de prise en charge[END_REF][START_REF] Has | Artériopathie des membres inférieurs[END_REF][START_REF] Has | Maladie coronarienne stable[END_REF].This can be explained by a lack of communication about new publications. Considering the result of the study and given the cost of developing these guidelines, we can wonder whether another method of diffusion of medical information should be studied.

Limitations

In this study, there are several limitations. First, GPRs from 10 faculties did not have access to the questionnaire since the faculties did not agree to distribute the questionnaire.

Others limitations are similar to the GPs' study. We used a questionnaire rather than analyzing actual practice. Nevertheless, the clinical cases were developed in line with previous studies [START_REF] Charasson | Atherosclerosis knowledge -diagnosis and management in primary care[END_REF][START_REF] Streit | Awareness of Stroke Risk after TIA in Swiss General Practitioners and Hospital Physicians[END_REF]. The advantage of this methodology was that the questions were similar for the three diseases, which made it possible to compare the results and reduce bias.

Moreover, the clinical cases were not randomized due to technical considerations. At the end, we analyzed three specific clinical presentations of ICH, CHD and PAD, which do not represent all clinical presentations. Our results cannot be generalized to other clinical presentations.

Conclusion

As for general practitioners, this study reveals a difference in GPRs' knowledge depending on the location of the atherosclerosis. Considering the results, the improvement of initial training and CME in general medicine would be desirable. We can also encourage team-work with specialists that could lead to better patient care.

  Mai 2017 et Septembre 2017, les IMG ont reçu par mail le questionnaire précédemment utilisé pour évaluer les médecins généralistes sur la pathologie athéromateuse. Ce questionnaire comportait trois cas cliniques portant sur un accident ischémique transitoire, un angor stable et une claudication intermittente. Chaque cas clinique se composait de sept questions identiques. Le critère de jugement principal portait sur le nombre d'IMG ayant répondu correctement à cinq questions. Résultats Cinq cent cinquante-trois IMG (10 %) ont répondu entièrement au questionnaire. Le pourcentage de prises en charge en ade quation avec les recommandations de la Haute Autorite ́ de Sante ́ e tait significativement plus e ĺeve ́ pour l'AIT (19,9 %) comparativement a ̀ l'angor stable (0,9 %) et a ̀ la claudication intermittente (0,4 %). Le diagnostic a e te ́ majoritairement pose ́ par l'ensemble des IMG : 525 (94,9 %) ont retenu le diagnostic d'AIT, 513 (92,8 %) celui d'angor stable et 532 (96,2 %) celui de claudication intermittente. Les principales difficulte ś portaient sur les examens comple ḿentaires et le traitement.

  (2015)/LimeSurvey: An Open Source survey tool /LimeSurvey Project Hamburg, Germany. URL http://www.limesurvey.org). The different locations of atherosclerotic disease (ICD, CHD, PAD) were studied by their corresponding presentations outside emergency situations: transient ischemic stroke (TIA), stable angina (SA) and intermittent claudication (IC).

Acknowledgement:

We would like to thank Leanne Moloney who corrected our English .

the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS). Eur Heart J 2018;39:763-816. doi:10.1093/eurheartj/ehx095.


Robin Alais
email: robin.alais@mines-paristech.fr

Petr Dokládal
email: petr.dokladal@mines-paristech.fr

Ali Erginay
email: ali.erginay@aphp.fr

Bruno Figliuzzi
email: bruno.figliuzzi@mines-paristech.fr

Etienne Decencière
email: etienne.decenciere@mines-paristech.fr

Fast macula detection and application to retinal image quality assessment

Keywords: Image quality assessment, Convolutional neural networks, Retinal imaging, Macula Detection

Introduction

Problem Presentation

It is estimated that there were 415 million people with diabetes in 2015, and this number is expected to reach 642 million by 2040 [START_REF] Ogurtsova | IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040[END_REF]. A common complication of diabetes is diabetic retinopathy (DR), which is one of the main causes of blindness and visual loss [START_REF] Sjølie | Retinopathy and vision loss in insulin-dependent diabetes in Europe: the EURODIAB IDDM Complications Study[END_REF][START_REF] Matthews | UK Prospective Diabetes Study Group, Risks of progression of retinopathy and vision loss related to tight blood pressure control in type 2 diabetes mellitus: Ukpds 69[END_REF]. Due to the heterogeneity of protocols between different epidemiology studies, it is hard to give a precise prevalence of DR; however, the percentage of diabetic patients found with DR in recent studies is relatively stable and ranges from 21.9% to 36.8% [START_REF] Delcourt | Epidemiology of diabetic retinopathy: expected vs reported prevalence of cases in the french population[END_REF].

DR is detectable and treatable, but regular clinical examination of all patients diagnosed with diabetes is infeasible in practice; in many developing countries, there is a significant lack of ophthalmologists, and in developed countries, the number of people aged 60+ is growing at twice the rate of the profession [START_REF] Resnikoff | The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners[END_REF].

In most clinically significant cases, DR is detectable on eye fundus photographs. Telemedicine networks [START_REF] Choremis | Use of telemedicine in screening for diabetic retinopathy[END_REF][START_REF] Boucher | Teleophthalmology screening for diabetic retinopathy through mobile imaging units within Canada[END_REF][START_REF] Massin | OPHDIAT c : A telemedical network screening system for diabetic retinopathy in the Îlede-France[END_REF][START_REF] Tozer | Telemedicine and Diabetic Retinopathy: Review of Published Screening Programs[END_REF] have been created in various countries in order to perform mass screening; international and local guidelines recommend one funduscopic examination per year for diabetic patients [START_REF]Executive Summary: Standards of Medical Care in Diabetes-2012[END_REF][START_REF] Massin | OPHDIAT c : A telemedical network screening system for diabetic retinopathy in the Îlede-France[END_REF]. Photographs can be taken by technicians in hospitals, specific screening centers, pharmacies or even prisons equipped with non-mydriatic cameras. In recent years, portable retinographs have been developed, which allow for even more massive screening. Photographs are then sent to ophthalmologists, who grade them and indicate the course of action to be followed. Both patient and practician time can be saved this way, provided that images are of good enough quality.

Due to the increasing amount of data, in conjunction with the limited number of ophthalmologists, computer retinal image understanding is of utmost interest. The literature concerning eye fundus image processing is abundant (a non-exhaustive review can be found in [START_REF] Sreejini | A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images, International Journal of Image[END_REF]), including many segmentation methods to extract anatomical structures such as the optic disk, the macula or the vascular network, or pathological structures [START_REF] Agurto | A Multiscale Optimization Approach to Detect Exudates in the Macula[END_REF][START_REF] Niemeijer | Fast detection of the optic disc and fovea in color fundus photographs[END_REF][START_REF] Veras | Assessing the accuracy of macula detection methods in retinal images[END_REF][START_REF] Welfer | Fovea center detection based on the retina anatomy and mathematical morphology[END_REF][START_REF] Gupta | Detection of retinal hemorrhages in the presence of blood vessels[END_REF][START_REF] Zhang | Exudate detection in color retinal images for mass screening of diabetic retinopathy[END_REF], as well as automatic predictions of DR severity [START_REF] Quellec | Deep image mining for diabetic retinopathy screening[END_REF][START_REF] Xiao | Automatic non-proliferative diabetic retinopathy screening system based on color fundus image[END_REF][START_REF] Gardner | Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool[END_REF].

Image quality estimation is a necessary preliminary step to these tasks, since it would make little sense applying an automatic diagnosis algorithm to images too noisy, blurred or not contrasted enough. Most publicly available datasets, like the Kaggle Diabetic Retinopathy dataset or the Messidor database [START_REF] Decencière | FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE[END_REF], contain only gradable images, and in [START_REF] Niemeijer | Fast detection of the optic disc and fovea in color fundus photographs[END_REF], it is clearly mentioned that for building a local database, "acceptable image quality, as judged by the screening program ophthalmologists, was a selection criterion".

In the context of a telemedicine network, the diagnosis is performed by a human expert, but the photographs are taken at a different time and location, by operators whose skill and level of experience can vary. A significant portion of images -around 10% for the OPHDIAT network [START_REF] Massin | OPHDIAT c : A telemedical network screening system for diabetic retinopathy in the Îlede-France[END_REF] -are deemed uninterpretable by ophthalmologists. This could be prevented by automatically estimating the quality at acquisition time, sending a warning to the operator if the photograph should be re-taken.

For a diagnosis to be made, an essential requirement is that the region of the macula must be clearly visible. In the present work, we focus on this task and present a lightweight CNN architecture capable of evaluating if the macula is 1) clearly visible 2) entirely within the field of view. In addition, our algorithm is able to accurately locate the macula if both conditions are met. This position can be used as well to check if a central image is correctly centered on the macula, or as part of an automated diagnosis algorithm, where the distance between lesions and the macula is an important information.

Related Work

So far, proposed methods for locating the macula either require images to be of sufficient quality [START_REF] Niemeijer | Fast detection of the optic disc and fovea in color fundus photographs[END_REF], or attempt at providing a location based on contextual information like the optic disk and vascular network, even if the macula itself is not visible in the image [START_REF] Veras | Assessing the accuracy of macula detection methods in retinal images[END_REF][START_REF] Welfer | Fovea center detection based on the retina anatomy and mathematical morphology[END_REF]. The originality of our approach lies in the fact that we use real clinical data, including very low-quality images; we automatically assess image quality, and we deliberately aim at detecting the macular region only if its visibility is sufficient.

The earliest attempts at defining a score for eye fundus image quality relied on properties of intensity histograms [START_REF] Lee | Automatic retinal image quality assessment and enhancement[END_REF][START_REF] Lalonde | Automatic visual quality assessment in optical fundus images[END_REF]. Image structure clustering, introduced in [START_REF] Niemeijer | Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening[END_REF], applies a bank of filters in order to perform unsupervised segmentation into several clusters roughly corresponding to anatomical structures of the retina, such as optic disk, vessels or retinal background. In this article, authors summarize a retinal photograph as a 20-dimensional vector composed of the histogram of the image structure clusters (5 bins), along with the three histograms of each color plane (5 bins each); good quality images are then separated from bad quality images by means of a Support Vector Machine. A similar approach was used in [START_REF] Paulus | Automated quality assessment of retinal fundus photos[END_REF], although for images centered on the optic nerve of only 22.5 • field of view. The authors also used a Support Vector Machine as their final classifiers, but they use Haralick [START_REF] Haralick | Textural features for image classification[END_REF] and sharpness features.

Other features have been proposed, such as the density of visible blood vessels, either in the whole image [START_REF] Giancardo | Elliptical local vessel density: a fast and robust quality metric for retinal images[END_REF][START_REF] Giancardo | Quality assessment of retinal fundus images using elliptical local vessel density[END_REF] or near the macula [START_REF] Hunter | An automated retinal image quality grading algorithm[END_REF]. Measures of clarity [START_REF] Fleming | Automated clarity assessment of retinal images using regionally based structural and statistical measures[END_REF] and blurring [START_REF] Pires | Retinal Image Quality Analysis for Automatic Diabetic Retinopathy Detection[END_REF] have also been defined. Finally, some methods combine both general image features and retina-specific ones, making use of vessel density, histogram, textural, and local sharpness [START_REF] Yu | Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening[END_REF][START_REF] Dias | Retinal image quality assessment using generic image quality indicators[END_REF][START_REF] Dias | Evaluation of Retinal Image Gradability by Image Features Classification[END_REF].

In recent years, convolutional neural networks have been proven very efficient on difficult computer vision tasks, notably winning the ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2012 competition [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. CNNs have then been applied to various tasks, including segmentation of retinal images [START_REF] Maninis | Deep retinal image understanding[END_REF][START_REF] Al-Bander | Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc[END_REF], glaucoma and DR grading [START_REF] Pratt | Convolutional neural networks for diabetic retinopathy[END_REF][START_REF] Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF]40].

In this context, estimating retinal image quality with convolutional neural networks is an interesting research di-rection. In [START_REF] Mahapatra | A CNN based neurobiology inspired approach for retinal image quality assessment[END_REF], a convolutional network is trained in order to discriminate gradable images from artificial ungradable images obtained by adding noise to the original images of the DRISHTI dataset [START_REF] Sivaswamy | A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis[END_REF], which contains 101 acquisitions centered on the optic disk, with a 30 • field of view, all images being taken with the pupils dilated. In [START_REF] Tennakoon | Image Quality Classification for DR Screening Using Convolutional Neural Networks[END_REF], the same authors have experimented with both a 'shallow' network (the total number of weights cannot be calculated, since the number of neurons in the two fully-connected layers is not given, but the weights in the convolutional layers alone exceed 1 million) trained from scratch, and AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] fine-tuned on a dataset consisting in 908 ungradable and 944 gradable non-mydriatic images. On a larger dataset (9653 ungradable retinal images and 11347 gradable images), they also evaluated the possibility of using a hybrid method combining saliency maps and CNNs [START_REF] Mahapatra | Retinal Image Quality Classification Using Saliency Maps and CNNs[END_REF]. Finally, [START_REF] Sun | Retinal Image Quality Classification Using Fine-Tuned CNN[END_REF] compare the performance of fine-tuning four CNN architectures -AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF], GoogLeNet [START_REF] Szegedy | Going deeper with convolutions[END_REF], VGG-16 [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] and ResNet-50 -on a 3000-image subset of the Kaggle database. These preliminary studies report that large networks are hard to train, and must deal with overfitting issues, due to the huge amount of parameters. In an attempt to overcome this problem, data augmentation is extensively used, to the point where so much distortion is introduced that most of the training data is not constituted of real or even realistic examples. An example of this is generating artificial new data by applying large rotations (up to 210 • !) to images; the resulting images are unrealistic, and it makes the network more or less rotational invariant, which is not a desirable feature for analyzing eye fundus images.

Another drawback of very large networks is their integration in embedded systems. Several million weights can constitute a significant fraction of the available memory: 233MB for AlexNet, 528MB for VGGNet for weights and biases alone, and the prediction times on embedded CPUs can be on the order of a second [START_REF] Lu | Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices[END_REF].

In this work, we propose a lightweight solution, with only 8329 parameters, and a reduced number of convolutions to be performed, meaning low power consumption as well. A comparison of memory requirements and computation times on embedded systems between our algorithm and other classic convolutional networks is given in Table 1. Our algorithm, including disk access and postprocessing, was benchmarked on a Raspberry Pi; timings for the other networks come from [START_REF] Lu | Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices[END_REF] and were obtained on a more powerful 1.9GHz quad-core ARM Cortex-A57 64bit CPU (NVIDIA TX1). For a given task, we should expect it to be performed faster on the TX1 than on the Raspberry Pi. Despite this, our algorithm is the fastest, being more than three times as fast as ResNet, and more than 17 times as fast as VGGNet. It is also the lightest by far, requiring only 98kB for storing the network's weights. We have also benchmarked a modified version of the segmentation network U-Net [START_REF] Ronneberger | U-Net: Convolutional Networks for Biomedical Image Segmentation[END_REF], whose performance for our task is evaluated in section 4. [START_REF] Lu | Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices[END_REF] on a NVIDIA Jetson TX1. Our network and our implementation of U-Net were benchmarked on a Raspberry Pi; it should be expected that they would have run faster on the TX1.

Page 4 of 11

Database

We extracted 6098 eye fundus images from the e-ophtha database [START_REF] Decencière | TeleOphta: Machine learning and image processing methods for teleophthalmology[END_REF]. This database has itself been extracted from the OPHDIAT telemedicine network for DR screening. These images are either central (centered on the macula), or nasal (centered on the optic disk). Different retinographs were used for the acquisitions, with resolutions ranging from 1440x960 to 3504x2336 pixels. Two different readers, independently from each other, indicated whether or not the macula was both entirely within the field of view, and clearly visible, meaning that the fovea and the small vessels around the avascular region can be seen. When that was the case, the fovea's position (xand y-coordinates) was labeled. When there was a disagreement on the macula's visibility, a decision was made on which annotation to use. Out of the 6098 images that were considered, the macula was deemed visible by both readers on 3142. The remaining images correspond either to bad quality central images or to nasal images where the macula is at least partly outside the field of view (FOV). Sample images are shown in Fig. 1.

Methodology

Image Preprocessing

Briefly put, the idea is to train a network to segment the macular region: with an ideal algorithm, if the segmentation is unsuccessful, it means that no macula is visible; if the segmentation succeeds, it means that image quality in the macular region was sufficient, and we immediately get fovea localization as a byproduct.

To train the network, we used the green channel, which was cropped, zero-padded in order to retain a square frame when necessary, and resized to a 128x128 image (see Fig. 2). This can seem aggressive, and some details might be lost, but approaches for assessing the severity of DR use networks with inputs as small as 512x512 [START_REF] Quellec | Deep image mining for diabetic retinopathy screening[END_REF], even though the task is much more complex and can rely on the presence of small structures like microaneurysms. A previous work for localizing both macula and optic disk in goodquality images used as input the green channel resized to 256x256 [START_REF] Al-Bander | Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc[END_REF]. The aim of this work is a bit different, since we are less interested in predicting a precise location -although our algorithm never predicts a location outside the macula (see Sec. 4.2) -but rather in estimating the quality of the macular region. A 128x128 resolution is sufficient to visually assess the visibility of the macula, and means that smaller networks can be used, easier to train and less costly to run in an embedded framework, in real time.

Where the macula was visible, we used the mean of the two annotations as reference for the fovea's position; in 128x128 resolution, the average distance between readers was 1.25 pixels. We considered the macula to be about 20 pixels wide, and we used as ground truth a disk of ones of radius 10 pixels, the rest of the image being set to zero. When the macula was not visible because of low image quality or because it was at least in part outside the FOV (Fig. 1c and1d), the ground truth was an image of zeros.

The only pre-processing consisted in dividing the (8bit) images by 255, in order to get images valued between 0 and 1. No contrast enhancement or filtering was applied, since we wanted to evaluate the quality and macula visibility of raw images. Data augmentation was used by applying vertical symmetries (transforming a right eye into a left eye or the other way around), but no horizontal symmetries or rotations were used, to avoid creating unrealistic training data.

The dataset was split in three parts: a training set, used for learning network parameters, a validation set, used to estimate network performance during learning and setting some hyperparameters, and a test set, exclusively used to assess the method's performance. Images corresponding to the same patient are necessarily in the same set in order to avoid any evaluation bias. The number of images in each set is given in Table 2.

Additionally, in order to further estimate the general-Page 5 of 11 Image (c) is the ground truth used to train our convolutional networks.

Network architecture

We trained a fully-convolutional network consisting only of 3x3 convolutional layers (each followed by a rectified linear unit) piled up, with all convolutional layers having 8 channels. We used zero-padding at every step, so that the output image is the same size as the input one. A similar architecture has been shown to provide good results for cell nucleus segmentation [START_REF] Pang | Cell nucleus segmentation in color histopathological imagery using convolutional networks[END_REF].

The number of convolutional layers we pile up determines the receptive field of each pixel in the output image: that is, if there are L layers in the network (including the output layer), the value of one pixel in the output image depends on the values in a 2L+1 by 2L+1 square centered at this position in the input image. We tried different values of L, ranging from 10 to 20; the best validation loss was achieved for networks with L = 16. The receptive field is then 33x33 pixels. When centered on a pixel at the edge of the macula, it contains the fovea and most -but not all -of the ground truth macular region.

This network has very few parameters: each channel of the first layer is defined by a 3x3 convolutional kernel and a bias term. In the following layers, each channel is defined by a 3x3x8 convolutional kernel and an extra bias term (73 parameters). Finally, the output layer is defined by a 3x3x8 convolutional kernel and a bias term. The network has a total 8329 parameters, which is extremely few (in comparison, AlexNet has 60 million parameters).

The network was initialized with truncated normal distributions with standard deviation σ = 0.1 for the convolution weights and zeros for the biases. The objective function we minimized was the L 2 distance between images. The gradient was estimated at each step on a mini-batch of 8 images, using the RMSProp optimizer [START_REF] Hinton | Rmsprop: Divide the gradient by a running average of its recent magnitude, Neural networks for machine learning[END_REF]. The network was trained for 4000 epochs.

Network Output Post-processing

Given an input 128x128 gray-scale image, the network outputs a 128x128 nonnegative array. A rectified linear unit is used in the output layer. We did also experiment using a sigmoid output activation, along with logistic loss, but it turned out that network convergence was harder to reach, and the obtained performance was lower.

Based on an output image, we have to answer the question "Is the macula visible in the original image ?". Since the ground truth image for a positive instance is a binary disk of radius 10, this is equivalent to answering the (ill-defined) question "Does the output image look like a binary disk of radius 10 ?".

The strategy we implemented goes as follows: the output image is thresholded above a value t in order to obtain a binary image. Then, only connected components of area greater than a value A are kept. If there is exactly one component remaining, we assume that the macula is visible, and the fovea can be located as the centroid of this component. If there is zero component of large enough area, we assume that the macula is not visible. If there are two or more components with area greater than A, this means that the algorithm is behaving oddly, and since our application is sensitivity-driven (it is important to correctly identify ungradable images), as a measure of precaution, we also consider that the macula is not clearly visible. Examples of network outputs and illustration of our post-processing are given in figure 3.

Results

Macula Visibility Estimation 4.1.1. Parameter Influence

There are two parameters to be set: the threshold t and minimum area A. In order to pick the best values Page 6 of 11 for these parameters, we looked at specificity (the fraction of images where the macula was annotated as visible correctly classified), sensitivity (the fraction of images where nothing was annotated classified as such) and overall accuracy on the validation set. Doing so on the training set could lead to overfitting, while doing so on the test set would give a biased estimation of the algorithm's ability to generalize. The specificity, sensitivity and accuracy curves are shown in Fig. 4. Unsurprisingly, the higher the threshold, the higher the sensitivity, but even taking t = 0.5 and A = 1 (i.e. demanding there is only one connected component after thresholding), almost 97% images where macula visibility was considered insufficient by human readers are correctly classified. The main observation is that for A ≤ 200, the choice of parameters actually has very little influence on the results. For the three considered thresholds, there is a steep fall when setting A above a certain value: in order to maximize sensitivity while keeping a certain margin to this critical value, we chose to set t = 0.9 and A = 200, which leads to a reasonable tradeoff, with 99% sensitivity, 95.3% specificity and 97.1% overall accuracy. We use those parameters in the following.

(a) (b) (c) (d) (e) (f) (g) (h)

Test Set Results

Accuracy on the test set reaches 96.4%. In comparison, the agreement ratio between the two annotators before a consensus was made was only of 89.9%. Sensitivity reaches 98.7%; in other words, out of 304 images where the macula was not annotated in the ground truth, the algorithm makes 4 mistakes. The images on which these errors are made can be seen in Fig. 5. As can be seen on the figure, these correspond, if not to annotating errors, at least to borderline cases. In two out of the four images, the small vessels around the macula can even be distinguished. Specificity reaches 94.2%: out of 310 images where the macula was annotated as clearly visible, the algorithm correctly classifies 292.

Performance on Pathological Images

Although we obtain a very good performance on our test set in terms of accuracy, sensitivity and specificity, it is interesting to look more specifically at images where automatic macula detection would be expected to be hard. Figure 6 shows examples of network outputs for images where lesions are present. Although there is one case of unsuccessful detection, we can see that the network is able to detect the macula even when there are hemorrhages or exudates in the macular zone.

U-Net Comparison

U-Net [START_REF] Ronneberger | U-Net: Convolutional Networks for Biomedical Image Segmentation[END_REF] is a popular network for segmentation and has been successfully used in a variety of applications. It makes perfect sense trying to apply it to our problem, however we have found it to slightly underperform compared to our network, in terms of both specificity and sensitivity. Several configurations of U-Net were tested. The best Page 7 of 11 one had 4 filters in the initial convolutional layer, and a gaussian noise layer at the end. On the test set, this network incorrectly predicts that the macula is not visible on 19 images, which is similar to our network (18 false negatives). The main drawback is that it has more than twice as many false positives (9 versus 4). It also has many more parameters (122,953) than our network. As for fovea localization, which is detailed in the next section, the average error for U-Net is 1.22 pixels, which is again more than our network's error (0.95 pixel).

Fovea Localization Results

Although the main task our algorithm addresses is assessing the quality of the macula region, it can also be used to segment the macula, or localize the fovea, as mentioned in section 3.3. In this section, we evaluate the performance of our algorithm for localizing the fovea, on the database we extracted from the e-ophtha database and on the ARIA database.

e-ophtha Database

As mentioned in the previous section, if we use t = 0.9 and A = 200, our algorithm predicts 292 images where the macula is visible, out of the 310 images where it was annotated. By lowering either parameter or defining another strategy, we could predict a location for the macula for more images, but it would make little sense in this context trying to localize it if we are not even confident it is in the FOV. In order to remain consistent, we leave the parameters unchanged and present localization results only on the images identified by our algorithm.

As previously mentioned in section 3. mean of the two annotators' positions is used as ground truth. The histogram of distances to the ground truth is shown in Fig. 7.

The average test error is 0.95 pixel, which is less than the average distance between the annotators. One pixel in 128x128 resolution represents 0.075 mm. The largest test error is 4.85 pixels, or 0.33 mm; it has to be noted that, since the macula was considered to be 10 pixels in radius, all of the predicted values lie within the macula.

ARIA Database C

The ARIA database C contains 61 central eye fundus images with corresponding ground truth annotations for optic disk and fovea. Our network, with the same postprocessing parameters as before, predicts a position for 46 of them, with a mean error of 1.4 pixel (0.1 mm) and a maximum error of 6 pixels (0.44 mm). The average error is again comparable to the average distance between two human readers (1.25 pixels, as mentioned in Sec. 2) and the maximum error is about half the macula's radius.

The 15 remaining images correspond to poor quality acquisitions; some examples can be seen in Fig. 8. Although it is quite easy for an experimented human observer to approximately locate the fovea region, the macular regions of these images are clearly of limited to no interest for an ophthalmologist.

Discussion

Guidelines for teleophthalmology recommend taking two photographs per eye, one centered on the macula, the other centered on the optic disk. A mandatory condition for a couple of images to be gradable by an ophthalmologist is that the macula is clearly visible in at least one of the two images. In practice, a significant proportion of examinations -about 10% for the OPHDIAT network -does not meet this required quality criterion. The algorithm detailed in the present work could significantly reduce the fraction of ungradable central images. Since the algorithm also provides the location of the macula, it can also be used in order to assert that a central image is indeed well Page 9 of 11 centered around it. It can also be used as part of an automated diagnosis algorithm, for which macula segmentation is often a crucial step [START_REF] Zhang | Image processing methods for computer-aided screening of diabetic retinopathy[END_REF].

Eye fundus photographs so far are often made by healthcare professionals, using tabletop non-mydriatic cameras; however, recent years have seen the emergence of portable, handheld retinographs, cheaper and allowing for screening in remote locations. These generally produce images of lower quality than tabletop retinographs. The algorithm described in the present work is very lightweight: it requires very little memory for storage, is very fast, and consumes little energy, compared to the much larger stateof-the-art networks. It can easily be integrated in an embedded device, telling in real time the operator whether another acquisition should be made.

Conclusion

In the present work, we use a fully-convolutional network to segment the macular region, in order to assess the quality of eye fundus images. Our algorithm is also able to provide fovea localization, within 0.1 mm of human performance in average, in the case image quality is deemed sufficient.

Although macula visibility is not the only requirement for a retinal image to be gradable for DR, the method we propose is able to significantly reduce the number of ungradable images sent to medical experts in teleophthalmology networks, saving both patient and physician time, when the number of ophthalmologists is insufficient for current needs and the diabetic population is expected to grow much faster than that of the profession. It can also be combined with other quality criteria, such as contrast and sharpness, to build a complete quality assessment system.

Figure 1 :

 1 Figure 1: Sample images from the e-ophtha dataset: the macula is considered clearly visible and entirely within the FOV in both (a) and (b). It is in the center of image (c) but the quality is insufficient for grading, and it is partly outside the FOV in (d), which is a good quality nasal image.

  izability of localization performance, we will use the ARIA Database C 8, for which fovea localization ground truth is available, as an additional test set.

Figure 2 :

 2 Figure 2: The green channel of the original color image (a) has been cropped around the region of interest, zero-padded on top and bottom so as not to introduce distortion, then resized to 128x128 (b). Image (c) is the ground truth used to train our convolutional networks.

Figure 3 :

 3 Figure 3: (a-b): input images, (c-d): corresponding network outputs,(e-f): thresholded network outputs (t = 0.9), (g-h): connected components of area greater than A = 200 pixels. These images belong to the test set and were chosen specifically because more than one component remained after thresholding, but this actually happens in only about 5% cases.

  (a) Algorithm specificity on the validation dataset plotted against A for different threshold values. (b) Algorithm sensitivity on the validation dataset plotted against A for different threshold values. (c) Algorithm accuracy on the validation dataset plotted against A for different threshold values.

Figure 4 :

 4 Figure 4: Influence of the threshold t and the minimum area A on the classification performance.

Figure 5 :

 5 Figure 5: The four test set images for which the algorithm incorrectly predicts that the zone of the macula is of good quality.

  3, we use the centroid of the (only) connected component of the thresholded output image as our estimation for fovea location. The Page 8 of 11

Figure 6 :

 6 Figure6: Network outputs for pathological images. In the four cases, there are hemorrhages and/or exudates in the macular region. Despite this, the network provides a reasonable output in the first three cases (the areas after thresholding at t = 0.9 are respectively 274, 91 and 250). The last case is an example of unsuccessful detection.

Figure 7 :

 7 Figure 7: Histogram of distances to ground truth on the test base (in pixels after resizing to 128x128).

Figure 8 :

 8 Figure 8: Examples of images from the ARIA database for which the network does not return a fovea position.

Table 1 :

 1 Computation times and memory use of various convolutional networks on embedded systems. The first four networks have been benchmarked in

	Network	Time (ms)	Time (ms) Weights (MB)
		TX1 (CPU)	Rasp.Pi
	AlexNet	893		233
	VGGNet	2809		528
	GoogLeNet	638		26
	ResNet	567		97
	Us		161	0.1
	U-Net		92	0.6

Table 2 :

 2 Number of images in the training, validation and test sets.

		Training Validation Test	All
	Visible	2193	639	310 3142
	Not visible	2056	596	304 2956
	Total	4249	1235	624 6098

Acknowledgements

This work was funded by the French "Fonds Unique Interministériel" through the RetinOpTIC project and sup-ported by the Medicen and Systematic competitive clusters.