Active and inactive microaneurysms identified and characterized by structural and angiographic optical coherence tomography

Purpose: To characterize flow status within microaneurysms (MAs) and quantitatively investigate their relations with regional macular edema in diabetic retinopathy (DR). Design: Retrospective, cross-sectional study. Participants: A total of 99 participants, including 23 with mild nonproliferative DR (NPDR), 25 with moderate NPDR, 34 with severe NPDR, 17 with proliferative DR. Methods: In this study, 3x3-mm optical coherence tomography (OCT) and OCT angiography (OCTA) scans with a 400x400 sampling density from one eye of each participant were obtained using a commercial OCT system. Trained graders manually identified MAs and their location relative to the anatomic layers from cross-sectional OCT. Microaneurysms were first classified as active if the flow signal was present in the OCTA channel. Then active MAs were further classified into fully active and partially active MAs based on the flow perfusion status of MA on en face OCTA. The presence of retinal fluid near MAs was compared between active and inactive types. We also compared OCT-based MA detection to fundus photography (FP) and fluorescein angiography (FA)-based detection. Results: We identified 308 MAs (166 fully active, 88 partially active, 54 inactive) in 42 eyes using OCT and OCTA. Nearly half of the MAs identified straddle the inner nuclear layer and outer plexiform layer. Compared to partially active and inactive MAs, fully active MAs were more likely to be associated with local retinal fluid. The associated fluid volumes were larger with fully active MAs than with partially active and inactive MAs. OCT/OCTA detected all MAs found on FP. While not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP. Conclusions: Co-registered OCT and OCTA can characterize MA activities, which could be a new means to study diabetic macular edema pathophysiology

UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.Â

An Ensemble Classifier Based on Individual Features for Detecting Microaneurysms in Diabetic Retinopathy

Individuals with diabetes are more likely to develop Diabetic Retinopathy (DR), a chronic ailment that can lead to blindness if left undiagnosed. Early-stage Diabetic Retinopathy (DR) is characterized by Microaneurysms (MA), which appear as tiny red lesions on the retina. This paper investigates a unique approach for the automated early identification of microaneurysms  in eye images. A unique ensemble classifier technique is suggested in this work. Classifiers like SVM, KNN, Decision Tree, and Naïve Bayes are chosen in this study for building an ensemble model. After preprocessing the image, certain common image characteristics such as shape and intensity features were retrieved from the candidate. The mean absolute difference of each feature is computed. Based on mean ranges that would give improved classification results, an expert classifier is chosen and trained. The outputs of the classifiers are integrated for each of the distinct characteristics, and the number of categories that have been most frequently repeated is utilized to reach a final decision. The process has been comprehensively validated using two available open datasets, like e-ophtha and DIARETDB1. On the e-ophtha and DIARETDB1 datasets, the ensemble model achieved an AUC of 0.928 and 0.873, Sensitivity of 90.7% and 85%, Specificity of 90% and 91% respectively

Phenotypic Differences in a PRPH2 Mutation in Members of the Same Family Assessed with OCT and OCTA

Choroidal dystrophies comprise a group of chorioretinal degenerations. However, the different findings observed among these patients make it difficult to establish a correct clinical diagnosis. The objective of this study was to characterize new clinical findings by optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in these patients. Four family members with a PRPH2 gene mutation (p.Arg195Leu) were included. OCT was performed at the macula, and the thickness of the outer and inner retina, total retina, and choroid was measured. The features of the vascular network were analyzed by OCTA. Patients showed a decreased outer nuclear layer in the avascular area compared with the controls. Two patients presented greater foveal and parafoveal degeneration of the outer retina, whereas the most degenerated area in the rest was the perifovea. Disruption of the third outer band at the foveola is one of the first-altered outer bands. Slow blood flow areas or capillary dropout were main signs in the deep capillary plexus. Microaneurysms were frequently observed in less degenerated retinas. Vascular loops and intraretinal microvascular abnormalities (IRMAs) were present in the superficial plexus. Extensive degeneration of the choriocapillaris was detected. Phenotypic differences were found between patients: two showed central areolar choroidal dystrophy and the rest had extensive chorioretinal atrophy. These signs observed in OCT and OCTA can help to more appropriately define the clinical disease in patients with choroidal dystrophies.This research was funded by grants from the Spanish Ministry of Science and Innovation (FEDER- PID2019-106230RB-I00, RD16/0008/0001), Spanish Ministry of Universities (FPU16/04114 and FPU18/02964), Instituto de Salud Carlos III (RETICS-FEDER RD16/0008/0016), Asociación Retina Asturias/Cantabria, FARPE-FUNDALUCE, and Generalitat Valenciana (IDIFEDER/2017/064)

Deep Learning of Diabetic Retinopathy Classification in Fundus Images

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed classification model is divided into three major phases, including pre-processing, training the Resnet-50 network, and classification with evaluation. In the first phase, pre-processing techniques are applied to the APTOS2019 fundus images dataset to find the best features and highlight some fine details of these images. The resnet-50 network was trained in the second phase using the training set and saved the best model obtained that gives high accuracy during the training process. Finally, this saved model has been implemented on the testing dataset for classification DR grades. The proposed model shows good and best classification performance, which was obtained with an accuracy of 98.3%, a precision of 98.4%, an F1-Score of 98.5 % and the recall of 98.4%.