ACHIKO-D350: A dataset for early AMD detection and drusen segmentation

Age related macular degeneration is the third leading cause of global blindness. Its prevalence is increasing in these years for the coming of ”aging population”. Early detection and grading can prevent it from becoming severe and protect vision. Drusen is an important indicator for AMD. Thus automatic drusen detection and segmentation has attracted much research attention in the past years. However, a barrier handicapping the research of drusen segmentation is the lack of a public dataset and test platform. To address this issue, in this paper, we publish a dataset, named ACHIKO-D350, with manually marked drusen boundary. ACHIKO-D350 includes 254 healthy fundus images and 96 fundus images with drusen. The images with drusen cover a wide range of types, including images with sparsely distributed drusen or clumped drusen, images of poor quality, and both well macular centered images and mis-centered images. ACHIKO-D350 will be used for performance evaluation of drusen segmentation methods. It will facilitate an objective evaluation and comparison

Adaptive Super-Candidate Based Approach for Detection and Classification of Drusen on Retinal Fundus Images

Identification and characterization of drusen is essential for the severity assessment of age-related macular degeneration (AMD). Presented here is a novel super-candidate based approach, combined with robust preprocessing and adaptive thresholding for detection of drusen, resulting in accurate segmentation with the mean lesion-level overlap of 0.75, even in cases with non-uniform illumination, poor contrast and con- founding anatomical structures. We also present a feature based lesion- level discrimination analysis between hard and soft drusen. Our method gives sensitivity of 80% for high specificity above 90% and high sensitivity of 95% for specificity of 70% on representative pathological databases (STARE and ARIA) for both detection and discrimination

Effective Drusen Localization for Early AMD Screening using Sparse Multiple Instance Learning

Age-related Macular Degeneration (AMD) is one of the leading causes of blindness. Automatic screening of AMD has attracted much research effort in recent years because it brings benefits to both patients and ophthalmologists. Drusen is an important clinical indicator for AMD in its early stage. Accurately detecting and localizing drusen are important for AMD detection and grading. In this paper, we propose an effective approach to localize drusen in fundus images. This approach trains a drusen classifier from a weakly labeled dataset, i.e., only the existence of drusen is known but not the exact locations or boundaries, by employing Multiple Instance Learning (MIL). Specifically, considering the sparsity of drusen in fundus images, we employ sparse Multiple Instance Learning to obtain better performance compared with classical MIL. Experiments on 350 fundus images with 96 having AMD demonstrates that on the task of AMD detection, multiple instance learning, both classical and sparse versions, achieve comparable performance compared with fully supervised SVM. On the task of drusen localization, sparse MIL outperforms MIL significantly

Detection and Classification of Diabetic Retinopathy Pathologies in Fundus Images

Diabetic Retinopathy (DR) is a disease that affects up to 80% of diabetics around the world. It is the second greatest cause of blindness in the Western world, and one of the leading causes of blindness in the U.S. Many studies have demonstrated that early treatment can reduce the number of sight-threatening DR cases, mitigating the medical and economic impact of the disease. Accurate, early detection of eye disease is important because of its potential to reduce rates of blindness worldwide. Retinal photography for DR has been promoted for decades for its utility in both disease screening and clinical research studies. In recent years, several research centers have presented systems to detect pathology in retinal images. However, these approaches apply specialized algorithms to detect specific types of lesion in the retina. In order to detect multiple lesions, these systems generally implement multiple algorithms. Furthermore, some of these studies evaluate their algorithms on a single dataset, thus avoiding potential problems associated with the differences in fundus imaging devices, such as camera resolution. These methodologies primarily employ bottom-up approaches, in which the accurate segmentation of all the lesions in the retina is the basis for correct determination. A disadvantage of bottom-up approaches is that they rely on the accurate segmentation of all lesions in order to measure performance. On the other hand, top-down approaches do not depend on the segmentation of specific lesions. Thus, top-down methods can potentially detect abnormalities not explicitly used in their training phase. A disadvantage of these methods is that they cannot identify specific pathologies and require large datasets to build their training models. In this dissertation, I merged the advantages of the top-down and bottom-up approaches to detect DR with high accuracy. First, I developed an algorithm based on a top-down approach to detect abnormalities in the retina due to DR. By doing so, I was able to evaluate DR pathologies other than microaneurysms and exudates, which are the main focus of most current approaches. In addition, I demonstrated good generalization capacity of this algorithm by applying it to other eye diseases, such as age-related macular degeneration. Due to the fact that high accuracy is required for sight-threatening conditions, I developed two bottom-up approaches, since it has been proven that bottom-up approaches produce more accurate results than top-down approaches for particular structures. Consequently, I developed an algorithm to detect exudates in the macula. The presence of this pathology is considered to be a surrogate for clinical significant macular edema (CSME), a sight-threatening condition of DR. The analysis of the optic disc is usually not taken into account in DR screening systems. However, there is a pathology called neovascularization that is present in advanced stages of DR, making its detection of crucial clinical importance. In order to address this problem, I developed an algorithm to detect neovascularization in the optic disc. These algorithms are based on amplitude-modulation and frequency-modulation (AM-FM) representations, morphological image processing methods, and classification algorithms. The methods were tested on a diverse set of large databases and are considered to be the state-of the art in this field

Multimodal imaging in age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness and affects approximately one in seven Australians aged 50 years and above. Currently, this complex condition is not easily and uniformly assessed. The signs of AMD differ between eyes and also occur in other macular disorders. This hinders accurate diagnosis and classification, which is fundamental to optimal patient care. Ocular imaging and visual function assessment have the potential to minimise the devastating consequences of disease through early detection. However, multiple devices are now commercially available and the impact of these technologies in clinical practice may not be straightforward. For instance, their usefulness may depend on accessibility and the operator’s knowledge and clinical skills. The impact on patient management, as well as alternative models of eye-care delivery, requires clarification. This thesis aims to explore the current and potential utility of imaging technologies (optical coherence tomography, infrared imaging, monochromatic retinal photography and fundus autofluorescence) in the assessment and management of AMD and other diseases of retinal pigment epithelium dysfunction. The findings show that optometrists self-describe high levels of practice competency and make ready use of imaging in everyday practice. However, they also unwittingly demonstrated low awareness of the evidence base in AMD. Furthermore, when their interpretation of images was tested using a series of case vignettes, their diagnostic accuracy as a group improved by only five per cent (from 61 per cent to 66 per cent); their tendency to refer increased by four per cent. These factors might be improved through education. A series of open-access, chair-side reference charts were consequently devised to help optometrists use imaging technologies more effectively in clinical practice. The additive contribution of multimodal structural and functional testing was particularly emphasised. Finally, a novel model of intermediate-tier eye-care in Australia was shown to substantially reduce the number of false positive cases or cases without a specific diagnosis. Interestingly, this model was acclaimed by reviewers as “scoring highly for originality and of international relevance”. Most excitingly, the thesis concludes with future directions regarding collaborative care and multimodal imaging, where detection of disease might be facilitated via a computational approach

Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

Age-related Macular Degeneration (AMD) is the most common cause of adult blindness in the developed world. This paper describes a new image mining technique to perform automated detection of AMD from color fundus photographs. The technique comprises a novel hierarchical image decomposition mechanism founded on a circular and angular partitioning. The resulting decomposition is then stored in a tree structure to which a weighted frequent sub-tree mining algorithm is applied. The identified sub-graphs are then incorporated into a feature vector representation (one vector per image) to which classification techniques can be applied. The results show that the proposed approach performs both efficiently and accurately

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

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

Eye as a window to the brain: investigating the clinical utility of retinal imaging derived biomarkers in the phenotyping of neurodegenerative disease.

Background Neurodegenerative diseases, like multiple sclerosis, dementia and motor neurone disease, represent one of the major public health threats of our time. There is a clear persistent need for novel, affordable, and patient‐acceptable biomarkers of these diseases, to assist with diagnosis, prognosis and impact of interventions. And these biomarkers need to be sensitive, specific and precise. The retina is an attractive site for exploring this potential, as it is easily accessible to non‐invasive imaging. Remarkable technology revolutions in retinal imaging are enabling us to see the retina in microscopic level detail, and measure neuronal and vascular integrity. Aims and objectives I therefore propose that retinal imaging could provide reliable and accurate markers of these neurological diseases. In this project, I aimed to explore the clinical utility of retinal imaging derived measures of retinal neuronal and vessel size and morphology, and determine their candidacy for being reliable biomarkers in these diseases. I also aimed to detail the methods of retinal imaging acquisition, and processing, and the principles underlying all these stages, in relation to understanding of retinal structure and function. This provides an essential foundation to the application of retinal imaging analysis, highlighting both the strengths and potential weaknesses of retinal biomarkers and how they are interpreted. Methods After performing detailed systematic reviews and meta‐analyses of the existing work on retinal biomarkers of neurodegenerative disease, I carried out a prospective, controlled, cross‐sectional study of retinal image analysis, in patients with MS, dementia, and ALS. This involved developing new software for vessel analysis, to add value and maximise the data available from patient imaging episodes. Results From the systematic reviews, I identified key unanswered questions relating to the detailed analysis and utility of neuroretinal markers, and diseases with no studies yet performed of retinal biomarkers, such as non‐AD dementias. I recruited and imaged 961 participants over a two‐year period, and found clear patterns of significance in the phenotyping of MS, dementia and ALS. Detailed analysis has provided new insights into how the retina may yield important disease information for the individual patient, and also generate new hypotheses with relation to the disease pathophysiology itself. Conclusions Overall, the results show that retinal imaging derived biomarkers have an important and specific role in the phenotyping of neurodegenerative diseases, and support the hypothesis that the eye is an important window to neurological brain disease

Retinal atrophy, inflammation, phagocytic and metabolic disruptions develop in the MerTK-cleavage-resistant mouse model

In the eye, cells from the retinal pigment epithelium (RPE) facing the neurosensory retina exert several functions that are all crucial for long-term survival of photoreceptors (PRs) and vision. Among those, RPE cells phagocytose under a circadian rhythm photoreceptor outer segment (POS) tips that are constantly subjected to light rays and oxidative attacks. The MerTK tyrosine kinase receptor is a key element of this phagocytic machinery required for POS internalization. Recently, we showed that MerTK is subjected to the cleavage of its extracellular domain to finely control its function. In addition, monocytes in retinal blood vessels can migrate inside the inner retina and differentiate into macrophages expressing MerTK, but their role in this context has not been studied yet. We thus investigated the ocular phenotype of MerTK cleavage-resistant (MerTKCR) mice to understand the relevance of this characteristic on retinal homeostasis at the RPE and macrophage levels. MerTKCR retinae appear to develop and function normally, as observed in retinal sections, by electroretinogram recordings and optokinetic behavioral tests. Monitoring of MerTKCR and control mice between the ages of 3 and 18  months showed the development of large degenerative areas in the central retina as early as 4 months when followed monthly by optical coherence tomography (OCT) plus fundus photography (FP)/autofluorescence (AF) detection but not by OCT alone. The degenerative areas were associated with AF, which seems to be due to infiltrated macrophages, as observed by OCT and histology. MerTKCR RPE primary cultures phagocytosed less POS in vitro, while in vivo, the circadian rhythm of POS phagocytosis was deregulated. Mitochondrial function and energy production were reduced in freshly dissected RPE/choroid tissues at all ages, thus showing a metabolic impairment not present in macrophages. RPE anomalies were detected by electron microscopy, including phagosomes retained in the apical area and vacuoles. Altogether, this new mouse model displays a novel phenotype that could prove useful to understanding the interplay between RPE and PRs in inflammatory retinal degenerations and highlights new roles for MerTK in the regulation of the energetic metabolism and the maintenance of the immune privilege in the retina