Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies

Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning

[Abstract]: Background and Objectives: Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some promising treatments have been proposed that effectively slow down its development, their effectiveness significantly diminishes in the advanced stages. This emphasizes the importance of large-scale screening programs for early detection. Nevertheless, implementing such programs for a disease like AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. For the characterization of the disease, clinicians have to identify and localize certain retinal lesions. All this motivates the development of automatic diagnostic methods. In this sense, several works have achieved highly positive results for AMD detection using convolutional neural networks (CNNs). However, none of them incorporates explainability mechanisms linking the diagnosis to its related lesions to help clinicians to better understand the decisions of the models. This is specially relevant, since the absence of such mechanisms limits the application of automatic methods in the clinical practice. In that regard, we propose an explainable deep learning approach for the diagnosis of AMD via the joint identification of its associated retinal lesions. Methods: In our proposal, a CNN with a custom architectural setting is trained end-to-end for the joint identification of AMD and its associated retinal lesions. With the proposed setting, the lesion identification is directly derived from independent lesion activation maps; then, the diagnosis is obtained from the identified lesions. The training is performed end-to-end using image-level labels. Thus, lesion-specific activation maps are learned in a weakly-supervised manner. The provided lesion information is of high clinical interest, as it allows clinicians to assess the developmental stage of the disease. Additionally, the proposed approach allows to explain the diagnosis obtained by the models directly from the identified lesions and their corresponding activation maps. The training data necessary for the approach can be obtained without much extra work on the part of clinicians, since the lesion information is habitually present in medical records. This is an important advantage over other methods, including fully-supervised lesion segmentation methods, which require pixel-level labels whose acquisition is arduous. Results: The experiments conducted in 4 different datasets demonstrate that the proposed approach is able to identify AMD and its associated lesions with satisfactory performance. Moreover, the evaluation of the lesion activation maps shows that the models trained using the proposed approach are able to identify the pathological areas within the image and, in most cases, to correctly determine to which lesion they correspond. Conclusions: The proposed approach provides meaningful information—lesion identification and lesion activation maps—that conveniently explains and complements the diagnosis, and is of particular interest to clinicians for the diagnostic process. Moreover, the data needed to train the networks using the proposed approach is commonly easy to obtain, what represents an important advantage in fields with particularly scarce data, such as medical imaging.Xunta de Galicia; ED481B-2022-025Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED481A 2021/140Xunta de Galicia; ED431G 2019/01This work was funded by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project; Ministerio de Ciencia e Innovación, Government of Spain, through RTI2018-095894-B-I00 and PID2019-108435RB-I00 research projects; Axencia Galega de Innovación (GAIN), Xunta de Galicia, ref. IN845D 2020/38; Conselleria de Cultura, Educación e Universidade, Xunta de Galicia, through Grupos de Referencia Competitiva, ref. ED431C 2020/24, the predoctoral grant ref. ED481A 2021/140, and the postdoctoral grant ref. ED481B-2022-025; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, is funded by Conselleria de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaria Xeral de Universidades (20%)

Towards Unsupervised Domain Adaptation for Diabetic Retinopathy Detection in the Tromsø Eye Study

Diabetic retinopathy (DR) is an eye disease which affects a third of the diabetic population. It is a preventable disease, but requires early detection for efficient treatment. While there has been increasing interest in applying deep learning techniques for DR detection in order to aid practitioners make more accurate diagnosis, these efforts are mainly focused on datasets that have been collected or created with ML in mind. In this thesis, however, we take a look at two particular datasets that have been collected at the University Hospital of North-Norway - UNN. These datasets have inherent problems that motivate the methodological choices in this work such as a variable number of input images and domain shift. We therefore contribute a multi-stream model for DR classification. The multi-stream model can model dependency across different images, can take in a variable of input of any size, is general in its detection such that the image processing is equal no matter which stream the image enters, and is compatible with the domain adaptation method ADDA, but we argue the model is compatible with many other methods. As a remedy for these problems, we propose a multi-stream deep learning architecture that is uniquely tailored to these datasets and illustrate how domain adaptation might be utilized within the framework to learn efficiently in the presence of domain shift. Our experiments demonstrates the models properties empirically, and shows it can deal with each of the presented problems. The model this paper contributes is a first step towards DR detection from these local datasets and, in the bigger picture, similar datasets worldwide

Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting

Automated Analysis of Retinal and Choroidal OCT and OCTA Images in AMD

La dégénérescence maculaire liée à l'âge (DMLA) est une maladie oculaire progressive qui se manifeste principalement au niveau de la rétine externe et de la choroïde. Le projet de recherche vise à déterminer si des mesures obtenues à partir d'images de tomographie par cohérence optique (OCT) et d'angiographie OCT (OCTA) peuvent être utilisées afin de fournir de nouvelles informations sur des biomarqueurs de la DMLA, ainsi qu’une méthode de détection précoce de la maladie. À cette fin, un appareil permettant l’OCT et l’OCTA a été utilisé pour imager des sujets DMLA précoces et intermédiaires, et des sujets témoins. À la configuration sélectionnée de l’appareil OCT, chaque acquisition d'un œil fournit un volume de données qui est constitué de 300 images transversales appelées B-scan. Au total, des acquisitions de 10 yeux de sujets atteints de DMLA précoce et intermédiaire (3000 images B-scan) et un cas de DMLA néovasculaire, 12 yeux de sujets âgés de plus de 50 ans (3600 images B-scan) et 11 yeux de sujets âgés de moins de 50 ans (3300 images B-scan) ont été obtenues. Cinq méthodes d'extraction de caractéristiques ont été reproduites ou développées afin de déterminer si des différences significatives au niveau de l’œil pouvaient être observées entre les sujets atteints de DMLA précoce et intermédiaire et les sujets témoins d’âge similaire. Grâce à des tests non paramétriques, il a été établi que deux méthodes connues d'extraction de biomarqueurs de la DMLA (analyse d’absence de signal de débit sanguin au niveau de la choriocapillaire et une méthode de segmentation des drusen) produisent des mesures qui montrent des différences significatives entre les groupes, et qui sont représentées de façon uniforme à travers le plan frontal de l’œil. Il a ensuite été souhaité de tirer parti des mesures et de générer un modèle de classification de la DMLA interprétable basé sur l'apprentissage automatique au niveau des B-scans. Des spectres de fréquence résultant de la transformé de Fourier rapide de séries spatiales dérivées de mesures considérées comme représentatives des deux biomarqueurs ont été obtenues, et utilisées comme caractéristiques pour former un classifieur de type forêt aléatoire et un classifieur de type forêt profonde. L'analyse en composantes principales (PCA) a été utilisée pour réduire la dimensionnalité de l’espace des caractéristiques, et la performance des modèles et l'importance des prédicteurs ont été évaluées. Une nouvelle méthode a été conçue qui permet une reconstruction 3D automatisée et une évaluation quantitative de la structure des signaux OCTA et ainsi des vaisseaux rétiniens. Des mesures représentatives des drusen et de la choriocapillaire ont été utilisées pour créer des modèles interprétables pour la classification de la DMLA précoce et intermédiaire. Alors que la prévalence mondiale de la DMLA augmente et que les appareils OCT deviennent plus disponibles, un plus grand nombre de personnes hautement qualifiées est nécessaire pour interpréter les informations médicales et fournir les soins cliniques appropriés. L'analyse et le classement du niveau de sévérité de la DMLA par des experts par le biais d'images OCT sont coûteux et prennent du temps. Les modèles proposés pourraient servir à automatiser la détection de la DMLA, même lorsqu'elle est asymptomatique, et signaler à un ophtalmologue la nécessité de surveiller et de traiter la condition avant la survenue de pertes graves de la vision. Les modèles sont transparents et sont en mesure de fournir une classification à partir d’une seule image transversale. Par conséquent, l'outil diagnostic automatisé pourrait également être utilisé dans des situations où seules des données médicales partielles sont disponibles ou lorsque l'accès aux ressources de soins de santé est limité.----------ABSTRACT Age-related macular degeneration (AMD) is a progressive eye disease which manifests primarily at the outer retina and choroid. The research project aimed to determine whether measures obtained from optical coherence tomography (OCT) and OCT angiography (OCTA) images could be used to provide novel AMD biomarker insight and an early disease detection method. To that end, an OCT and OCTA enabled device was used to image AMD subjects and controls. At the selected device scan size, each scan of one eye gathered using an OCT device provides a volume of data which is constructed of 300 cross-sectional images termed B-scans. In total, scans of 10 eyes from subjects with early and intermediate AMD (3,000 B-scan images) and a case of neovascular AMD, 12 eyes from subjects over the age of 50 years old (3,600 B-scan images), and 11 eyes from subjects under the age of 50 years old (3,300 B-scan images) were obtained. Five feature extraction methods were either reproduced or developed in order to determine if significant differences could be observed between the early and intermediate AMD subjects and control subjects at the eye level. Through non-parametric testing it was established that two AMD biomarker extraction methods (choriocapillaris flow voids analysis and a drusen segmentation method) produced measures which showed significant differences between groups, and which were also uniformly represented across the frontal plane of the eye. It was then desired to leverage the measures and generate a B-scan level, interpretable machine learning-based AMD classification model. Frequency spectrums resulting from the fast Fourier transforms of spatial series derived from measures believed to be representative of the two biomarkers were obtained and used as features to train a random forest and a deep forest classifier. Principal component analysis was used to reduce dimensionality of the feature space, and model performance and predictor importance were assessed. A new method was devised which allows automated 3D reconstruction and quantitative evaluation of retinal flow signal patterns and incidentally of retinal microvasculature. Measures representative of drusen and choriocapillaris were leveraged to create interpretable models for the classification of early and intermediate AMD. As the worldwide prevalence of AMD increases and OCT devices are becoming more available, a greater number of highly trained personnel is needed to interpret medical information and provide the appropriate clinical care. Expert analysis and grading of AMD through OCT images are expensive and time consuming. The models proposed could serve to automate AMD detection, even when it is asymptomatic, and signal to an ophthalmologist the need to monitor and treat the condition before the occurrence of severe visual loss. The models are transparent and provide classification from single cross-sectional images. Therefore, the automated diagnosis tool could also be used in situations where only partial medical data are available, or where there is limited access to health care resources

Ultrasensitive gold micro-structured electrodes enabling the detection of extra-cellular long-lasting potentials in astrocytes populations

Ultra-sensitive electrodes for extracellular recordings were fabricated and electrically characterized. A signal detection limit defined by a noise level of 0.3-0.4 mu V for a bandwidth of 12.5 Hz was achieved. To obtain this high sensitivity, large area (4 mm(2)) electrodes were used. The electrode surface is also micro-structured with an array of gold mushroom-like shapes to further enhance the active area. In comparison with a flat gold surface, the micro-structured surface increases the capacitance of the electrode/electrolyte interface by 54%. The electrode low impedance and low noise enable the detection of weak and low frequency quasi-periodic signals produced by astrocytes populations that thus far had remained inaccessible using conventional extracellular electrodes. Signals with 5 mu V in amplitude and lasting for 5-10 s were measured, with a peak-to-peak signal-to-noise ratio of 16. The electrodes and the methodology developed here can be used as an ultrasensitive electrophysiological tool to reveal the synchronization dynamics of ultra-slow ionic signalling between non-electrogenic cells.Portuguese Foundation for Science and Technology (FCT), through the project "Implantable organic devices for advanced therapies" (INNOVATE) [PTDC/EEI-AUT/5442/2014]; Instituto de Telecomunicacoes [UID/Multi/04326/2013]; Associated Laboratory - Institute of Nanoscience and Nanotechnology [POCI-01-0145-FEDER-016623]; [PTDC/CTM-NAN/3146/2014

Role of the BMP9/ALK1 pathway in the regulation of pathological and VEGF-mediated angiogenesis

L’angiogenèse est définie comme la formation de nouveaux capillaires à partir des vaisseaux sanguins pré-existants. Elle contribue à l’extension du réseau vasculaire et assure ainsi l’efficacité des échanges gazeux et du transport des cellules, nutriments, métabolites et molécules de signalisation vers les tissus. L’angiogenèse par bourgeonnement passe par la spécification d’une cellule endothéliale en cellule meneuse et la formation d’un réseau de cellules suiveuses à la base du bourgeon vasculaire. Toute perturbation de ce mode de néovascularisation génère des vaisseaux fortement tortueux, immatures et non-étanches qui soit affectent les fonctions physiologiques des organes en causant ainsi des pathologies potentiellement fatales, ou accélèrent la progression des conditions telles que le cancer. Dans l’oeil, l’angiogenèse pathologique des vaisseaux choroïdiens et rétiniens cause une perte de la vue. Particulièrement, la dégénérescence maculaire liée à l’âge (DMLA) de type néovasculaire, une maladie oculaire caractérisée par le bourgeonnement anormal de la choroïde dans l’espace sous-rétinien, représente la cause majeure de cécité au sein des populations des pays industrialisés. Les thérapies conventionnelles contre la DMLA humide reposent sur l’usage des médicaments qui ciblent la signalisation du facteur de croissance de l’endothélium vasculaire (VEGF). Bien que démontrant des résultats cliniques, ces traitements anti-VEGFs sont invasifs et présentent multiples effets secondaires. Par ailleurs, ils n’induisent aucun effet chez une portion des patients traités. De ces faits, il existe présentement un grand besoin de thérapies alternatives aux anti-VEGFs. De façon intéressante, la protéine de morphogénèse osseuse 9 (BMP9), qui active son récepteur “activin receptor-like kinase 1” (ALK1), régule l’angiogenèse développementale des vaisseaux rétiniens de l’oeil de la souris. Par ailleurs, les mutations au sein du BMP9, de son récepteur ALK1 ou de ses intermédiaires de signalisation sont associées à la morphogénèse anormale des vaisseaux qui contribue ultimement à la pathogénèse de diverses maladies néovasculaires. De façon additionnelle, le récepteur ALK1 au BMP9 est restreint à la cellule endothéliale; contrairement à ceux des ligands angiogéniques tels que le VEGF, exprimés par une diversité de cellules. Par ailleurs, au sein de cette cellule, le BMP9 contribue à la régulation des phénotypes meneur et suiveur qui sont induits par le VEGF et requis pour le déroulement de l’angiogenèse par bourgeonnement. De ces faits qui précèdent, nous avons émis l’hypothèse du rôle du BMP9 dans la régulation de la néovascularisation pathologique relative à la DMLA humide. Ainsi, les travaux de la présente thèse déterminent spécifiquement l’effet du BMP9 sur l’angiogenèse pathologique à l’aide des modèles oculaires pertinents à la DMLA humide et examine aussi sa base mécanistique. Les travaux de cette thèse démontrent l’effet anti-angiogénique du BMP9 sous les conditions expérimentales de néovascularisation choroïdienne induite au laser (CNV) et de rétinopathie induite par l’oxygène (OIR). Ils montrent aussi les effets régulateurs de la signalisation du BMP9 sur les voies de signalisation endothéliales du VEGF et de Notch, respectivement de façon dépendante de VEGFR1 et de JAG1. En somme, les présentes études démontrent les effets anti-angiogéniques du BMP9 sur la néovascularisation pathologique relative à la DMLA humide et identifient les facteurs moléculaires qui contribuent à son action inhibitrice du bourgeonnement vasculaire induit par le VEGF.Angiogenesis is defined as the formation of new capillaries from existing blood vessels. It extends the vasculature and thereby sustains the efficient exchange of gases and transport of cells, nutrients, metabolites and signalling molecules to tissues. Sprouting angiogenesis proceeds through the selective specification of an endothelial cell into a leading tip cell and the formation of stalk cells at the base of the sprout. A disturbance in this modality of neovascularisation leads to highly tortuous, immature and leaky vessels that either impair the physiological functions of organs, thereby causing life-threatening diseases, or accelerate the progression of conditions such as cancer. In the eye, the pathological angiogenesis of choroidal and retinal vessels specifically results in vision loss. Particularly, the neovascular form of the age-related macular degeneration (AMD), an ocular disease characterized by the abnormal sprouting of the choroidal network into the subretinal space, represents the leading cause of blindness in populations of industrialized countries. Conventional therapies against wet AMD are based on drugs that target the signaling of the vascular endothelium growth factor (VEGF). Despite their clinical achievements, the anti-VEGFs treatments are invasive and show multiple adverse effects. Moreover, they are not effective in a portion of treated patients. Thus, there currently is a substantial need of therapy alternatives to anti-VEGFs. Interestingly, the bone morphogenetic protein 9 (BMP9), that activates its activin receptor-like kinase 1 (ALK1) transducer, regulates the developmental angiogenesis of the mouse eye retina vasculature. Moreover, mutations in BMP9, its receptor ALK1 or its signaling mediators correlate with the abnormal vessel morphogenesis that ultimately drives the pathogenesis of various neovascular diseases. Additionally, the BMP9-specific receptor ALK1 is restricted to endothelial cells; in contrast to those of neovascularisation-inducing ligands such as VEGF, expressed by a range of cells. Particularly within these cells, BMP9 contributes to regulate the VEGF-induced tip/stalk phenotypes required for sprouting angiogenesis. Given the aforementioned, we hypothesized the role of BMP9 in regulating the pathological angiogenesis associated with wet AMD. Thus, the studies from the current thesis specifically determine the effect of BMP9 on pathological NV using ocular models relevant to AMD and further investigate its mechanical basis. The current work demonstrates the antiangiogenic effects of BMP9 under experimentally induced oxygen-induced retinopathy (OIR) and laser-induced choroid neovascularisation (CNV) conditions. Moreover, this thesis shows the regulatory effects of BMP9 signaling on the VEGF and Notch endothelial pathways, respectively in VEGFR1 and JAG1 -dependent manners. Collectively, the current studies demonstrate the anti-angiogenic effects of BMP9 on pathological NV associated with wet AMD and identify the molecular players that mediate its inhibitory action on VEGF-mediated sprouting

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 365)

This bibliography lists 211 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance