The evidence for automated grading in diabetic retinopathy screening

Peer reviewedPostprin

Regmentation: A New View of Image Segmentation and Registration

Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications

Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

Evaluation of Curcumin-Loaded Nanoliposomes for the Treatment and Prevention of Age-Related Macular Degeneration

Age-related macular degeneration (AMD), the most common cause of vision loss for people age 50 and over, is a disease characterized by the buildup of oxidative stress in the back of the eye. Current remedies are limited to intravitreal injections that only target the more severe ‘wet’ form; the common ‘dry’ form has no readily available pharmaceutical solution. Curcumin, a natural antioxidant found in the Indian spice turmeric, has shown potential in combating inflammatory diseases like AMD; however, the molecule also demonstrates poor bioavailability. This research aimed to create curcumin-loaded nanoliposomes (NLs) to be delivered noninvasively to potentially treat and prevent the onset of AMD. The 220 nm NLs were composed of phosphatidylcholine and cholesterol through vacuum evaporation, rehydration, and extrusion. Our curcuminloaded NLs were assessed using an in vitro oxidative stress model of ARPE-19 cells. MTT cell viability assay results show that the liposomal curcumin complex has been able to improve cell viability with respect to the untreated cells (28% more viable, p < 0.05), and cells that were damaged with peroxide (50% more viable, p < 0.05). As a preventative measure, the liposomal curcumin complex has been able to improve cell viability with respect to untreated cells (55% more viable, p < 0.05). Ex vivo modeling tested the permeability of the nanoliposomes to the posterior hemisphere of a porcine eye with a Franz diffusion cell apparatus. Qualitative fluorescence analysis shows that the nanoliposomes were able to permeate through different layers of the eye and reach the retina. In vitro studies with RPE cells show the treatment significantly reduces oxidative stress in cells while increasing cell viability, thus indicating that curcumin has potential to both treat and prevent AMD

Développement et validation d’un système automatique de classification de la dégénérescence maculaire liée à l’âge

RÉSUMÉ La dégénérescence maculaire liée à l’âge (DMLA) est une des principales causes de déficience visuelle menant à une cécité irréversible chez les personnes âgées dans les pays industrialisés. Cette maladie regroupe une variété d’anomalies touchant la macula, se présentant sous diverses formes. Un des moyens les plus couramment utilisés pour rapidement examiner la rétine est la photographie de fond d’œil. À partir de ces images, il est déjà possible de détecter et de poser un diagnostic sur l’avancée de la maladie. Une classification recommandée pour évaluer la DMLA est la classification simplifiée de l’AREDS qui consiste à diviser la maladie en quatre catégories : non-DMLA, précoce, modérée, et avancée. Cette classification aide à déterminer le traitement spécifique le plus optimal. Elle se base sur des critères quantitatifs mais également qualitatifs, ce qui peut entrainer des variabilités inter- et intra-expert. Avec le vieillissement de la population et le dépistage systématique, le nombre de cas de DMLA à être examinés et le nombre d’images à être analysées est en augmentation rendant ainsi le travail long et laborieux pour les cliniciens. C’est pour cela, que des méthodes automatiques de détection et de classification de la DMLA ont été proposées, afin de rendre le processus rapide et reproductible. Cependant, il n’existe aucune méthode permettant une classification du degré de sévérité de la DMLA qui soit robuste à la qualité de l’image. Ce dernier point est important lorsqu’on travaille dans un contexte de télémédecine. Dans ce projet, nous proposons de développer et valider un système automatique de classification de la DMLA qui soit robuste à la qualité de l’image. Pour ce faire, nous avons d’abord établi une base de données constituée de 159 images, représentant les quatre catégories de l’AREDS et divers niveaux de qualité d’images. L’étiquetage de ces images a été réalisé par un expert en ophtalmologie et nous a servi de référence. Ensuite, une étude sur l’extraction de caractéristiques nous a permis de relever celles qui étaient pertinentes et de configurer les paramètres pour notre application. Nous en avons conclu que les caractéristiques de texture, de couleur et de contexte visuel semblaient les plus intéressantes. Nous avons effectué par après une étape de sélection afin de réduire la dimensionnalité de l’espace des caractéristiques. Cette étape nous a également permis d’évaluer l’importance des différentes caractéristiques lorsqu’elles étaient combinées ensemble.----------ABSTRACT Age-related macular degeneration (AMD) is the leading cause of visual deficiency and legal blindness in the elderly population in industrialized countries. This disease is a group of heterogeneous disorders affecting the macula. For eye examination, a common used modality is the fundus photography because it is fast and non-invasive procedure which may establish a diagnostic on the stage of the disease. A recommended classification for AMD is the simplified classification of AREDS which divides the disease into four categories: non-AMD, early, moderate and advanced. This classification is helpful to determine the optimal and specific treatment. It is based on quantitative criteria but also on qualitative ones, introducing inter- and intra-expert variability. Moreover, with the aging population and systematic screening, more cases of AMD must be examined and more images must be analyzed, rendering this task long and laborious for clinicians. To address this problem, automatic methods for AMD classification were then proposed for a fast and reproducible process. However, there is no method performing AMD severity classification which is robust to image quality. This last part is especially important in a context of telemedicine where the acquisition conditions are various. The aim of this project is to develop and validate an automatic system for AMD classification which is robust to image quality. To do so, we worked with a database of 159 images, representing the different categories at various levels of image quality. The labelling of these images is realized by one expert and served as a reference. A study on feature extraction is carried out to determine relevant features and to set the parameters for this application. We conclude that features based on texture, color and visual context are the most interesting. After, a selection is applied to reduce the dimensionality of features space. This step allows us to evaluate the feature relevance when all the features are combined. It is shown that the local binary patterns applied on the green channel are the most the discriminant features for AMD classification. Finally, different systems for AMD classification were modeled and tested to assess how the proposed method classifies the fundus images into the different categories. The results demonstrated robustness to image quality and also that our method outperforms the methods proposed in the literature. Errors were noted on images presenting diabetic retinopathy, visible choroidal vessels or too much degradation caused by artefacts. In this project, we propose the first AMD severities classification robust to image quality