A self-supervised learning approach for registration agnostic imaging models with 3D brain CTA.

Deep learning-based neuroimaging pipelines for acute stroke typically rely on image registration, which not only increases computation but also introduces a point of failure. In this paper, we propose a general-purpose contrastive self-supervised learning method that converts a convolutional deep neural network designed for registered images to work on a different input domain, i.e., with unregistered images. This is accomplished by using a self-supervised strategy that does not rely on labels, where the original model acts as a teacher and a new network as a student. Large vessel occlusion (LVO) detection experiments using computed tomographic angiography (CTA) data from 402 CTA patients show the student model achieving competitive LVO detection performance (area under the receiver operating characteristic curve [AUC] = 0.88 vs. AUC = 0.81) compared to the teacher model, even with unregistered images. The student model trained directly on unregistered images using standard supervised learning achieves an AUC = 0.63, highlighting the proposed method's efficacy in adapting models to different pipelines and domains.

Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks.

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community.

Detection of Stroke with Retinal Microvascular Density and Self-Supervised Learning Using OCT-A and Fundus Imaging.

Acute cerebral stroke is a leading cause of disability and death, which could be reduced with a prompt diagnosis during patient transportation to the hospital. A portable retina imaging system could enable this by measuring vascular information and blood perfusion in the retina and, due to the homology between retinal and cerebral vessels, infer if a cerebral stroke is underway. However, the feasibility of this strategy, the imaging features, and retina imaging modalities to do this are not clear. In this work, we show initial evidence of the feasibility of this approach by training machine learning models using feature engineering and self-supervised learning retina features extracted from OCT-A and fundus images to classify controls and acute stroke patients. Models based on macular microvasculature density features achieved an area under the receiver operating characteristic curve (AUC) of 0.87-0.88. Self-supervised deep learning models were able to generate features resulting in AUCs ranging from 0.66 to 0.81. While further work is needed for the final proof for a diagnostic system, these results indicate that microvasculature density features from OCT-A images have the potential to be used to diagnose acute cerebral stroke from the retina.

Towards Stroke Biomarkers on Fundus Retinal Imaging: A Comparison Between Vasculature Embeddings and General Purpose Convolutional Neural Networks.

Fundus Retinal imaging is an easy-to-acquire modality typically used for monitoring eye health. Current evidence indicates that the retina, and its vasculature in particular, is associated with other disease processes making it an ideal candidate for biomarker discovery. The development of these biomarkers has typically relied on predefined measurements, which makes the development process slow. Recently, representation learning algorithms such as general purpose convolutional neural networks or vasculature embeddings have been proposed as an approach to learn imaging biomarkers directly from the data, hence greatly speeding up their discovery. In this work, we compare and contrast different state-of-the-art retina biomarker discovery methods to identify signs of past stroke in the retinas of a curated patient cohort of 2,472 subjects from the UK Biobank dataset. We investigate two convolutional neural networks previously used in retina biomarker discovery and directly trained on the stroke outcome, and an extension of the vasculature embedding approach which infers its feature representation from the vasculature and combines the information of retinal images from both eyes.In our experiments, we show that the pipeline based on vasculature embeddings has comparable or better performance than other methods with a much more compact feature representation and ease of training.Clinical Relevance-This study compares and contrasts three retinal biomarker discovery strategies, using a curated dataset of subject evidence, for the analysis of the retina as a proxy in the assessment of clinical outcomes, such as stroke risk.

NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation.

Glaucoma is an ocular disease threatening irreversible vision loss. Primary screening of Glaucoma involves computation of optic cup (OC) to optic disc (OD) ratio that is widely accepted metric. Recent deep learning frameworks for OD and OC segmentation have shown promising results and ways to attain remarkable performance. In this paper, we present a novel segmentation network, Nested EfficientNet (NENet) that consists of EfficientNetB4 as an encoder along with a nested network of pre-activated residual blocks, atrous spatial pyramid pooling (ASPP) block and attention gates (AGs). The combination of cross-entropy and dice coefficient (DC) loss is utilized to guide the network for accurate segmentation. Further, a modified patch-based discriminator is designed for use with the NENet to improve the local segmentation details. Three publicly available datasets, REFUGE, Drishti-GS, and RIM-ONE-r3 were utilized to evaluate the performances of the proposed network. In our experiments, NENet outperformed state-of-the-art methods for segmentation of OD and OC. Additionally, we show that NENet has excellent generalizability across camera types and image resolution. The obtained results suggest that the proposed technique has potential to be an important component for an automated Glaucoma screening system.

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.

Retinal image analysis for disease screening through local tetra patterns.

Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR) are the most prevalent diseases responsible for visual impairment in the world. This work investigates discrimination potential in the texture of color fundus images to distinguish between diseased and healthy cases by avoiding the prior lesion segmentation step. It presents a retinal background characterization approach and explores the potential of Local Tetra Patterns (LTrP) for texture classification of AMD, DR and Normal images. Five different experiments distinguishing between DR - normal, AMD - normal, DR - AMD, pathological - normal and AMD - DR - normal cases were conducted and validated using the proposed approach, and promising results were obtained. For all five experiments, different classifiers namely, AdaBoost, c4.5, logistic regression, naive Bayes, neural network, random forest and support vector machine were tested. We experimented with three public datasets, ARIA, STARE and E-Optha. Further, the performance of LTrP is compared with other texture descriptors, such as local phase quantization, local binary pattern and local derivative pattern. In all cases, the proposed method obtained the area under the receiver operating characteristic curve and f-score values higher than 0.78 and 0.746 respectively. It was found that both performance measures achieve over 0.995 for DR and AMD detection using a random forest classifier. The obtained results suggest that the proposed technique can discriminate retinal disease using texture information and has potential to be an important component for an automated screening solution for retinal images.