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

Processing of structural neuroimaging data in young children:bridging the gap between current practice and state-of-the-art methods

The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies

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

NGHIÊN CỨU NHÂN GIỐNG IN VITRO VÀ SỰ SINH TRƯỞNG PHÁT TRIỂN CÂY GIẢO CỔ LAM (GYNOSTEMMA PUBESCENS) TRONG NHÀ KÍNH

The Gynostemma pubescens plant, which is distributed mainly over the northern provinces, is a valuable and beneficial herb for human health. In this study, we investigated in vitro propagation and cultivation to evaluate the growth and development of Gynostemma pubescens in greenhouse conditions at Dalat city, Lamdong province. The results showed that MS medium supplemented with 1mg/l BA, 30 g/l sucrose, 8g/l agar, pH 5.8 was the best medium for shoot regeneration in vitro, with a shoot height of 1.84cm, and 10.50 shoots/explant. The MS medium contained 0.5mg/l TDZ, 30g/l sucrose, 8g/l agar, pH 5.8 was the most suitable medium for shoot regeneration in vitro, with a shoot height of 1.99cm, and 13.80 shoots/explant. Concentrations of IBA from 0 to 1 mg/l were appropriate for in vitro root regeneration, yielding a root regeneration rate of 100%. Coconut fiber powder was the most suitable substrate to transfer the plantlets to the greenhouse, with a survival rate of 100%, plant height of 9.73cm, and root length of 6.45cm. Humus proved the best substrate for plant development and growth, with a survival rate of 100%, plant height of 82.08cm, root length of 36.57cm and a fresh weight of 57.32g/plant. Watering with 100 ml of Nitrophoska fertilizer (2g/l) once a week was the best for plant growth, with a survival rate of 100%, height of 94.22cm, root length of 37.71cm, and a fresh weight of 59.38g/plant.Cây Giảo cổ lam (Gynostemma pubescens) phân bố chủ yếu tại các tỉnh phía Bắc, là một trong những loại thảo dược quí và tốt cho sức khỏe của con người. Trong nghiên cứu này, chúng tôi nghiên cứu nhân giống in vitro và nuôi trồng đánh giá sự sinh trưởng phát triển cây Giảo cổ lam cấy mô trong nhà kính tại Đà Lạt, Lâm Đồng. Kết quả cho thấy, môi trường MS (Murashige Skoog) bổ sung 1mg/l BA, 30g/l sucrose, 8g/l agar, pH 5.8 là tốt nhất đến sự tái sinh chồi in vitro, với chiều cao chồi 1.84 cm, số chồi 10.50 chồi/mẫu. Môi trường MS bổ sung 0.5 mg/l TDZ, 30g/l sucrose, 8 g/l agar, pH 5.8 là tốt nhất đến sự tái sinh chồi in vitro, với chiều cao chồi 1.99cm, số chồi 13.80 chồi/mẫu. Nồng độ IBA từ 0 - 1 mg/l đều thích hợp đến sự tái sinh rễ in vitro của cây Giảo cổ lam, với tỉ lệ tái sinh rễ đạt 100%. Vụn xơ dừa là giá thể thích hợp nhất để chuyển cây Giảo cổ lam cấy mô ra ngoài vườn ươm, với tỷ lệ sống đạt 100%, chiều cao cây 9.73cm, chiều dài rễ 6.45cm. Đất mùn là giá thể tốt nhất đến sự sinh trưởng phát triển của cây, với tỷ lệ sống đạt 100%, chiều cao cây 82.08cm, chiều dài rễ 36.57cm, khối lượng tươi 57.32g/cây. Tưới 100 ml phân Nitrophoska (2 g/l) theo định kỳ mỗi tuần một lần là tốt nhất đến sự sinh trưởng phát triển của cây, với tỷ lệ sống đạt 100%, chiều cao cây 94.22cm, chiều dài rễ 37.71cm, khối lượng tươi 59.38g/cây

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224 tr. ; 21 cm