Abstract
Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.
Highlights
Diabetic retinopathy (DR) is a microvascular disease that affects the 35% of the diabetic population [1, 2] and can cause rapid vision loss
We have developed a fuzzy c-means method that iteratively corrects the membership of pixels into healthy and defective categories by optimizing the number of clusters, until the pixels in a normative ratio image corresponding to a control group with healthy ellipsoid zone (EZ) has defect membership equal to zero
Performance of the fuzzy logic method was comparable to a previous method based on machine learning to detect EZ loss in ocular trauma [18] and Dice similarity to manual grading was better than a semiautomated segmentation method for EZ loss detection in retinal telangiectasia based on en face Optical coherence tomography (OCT) [22]
Summary
Diabetic retinopathy (DR) is a microvascular disease that affects the 35% of the diabetic population [1, 2] and can cause rapid vision loss. Abnormal retinal perfusion caused by microvascular damage after prolonged periods of high glucose levels can lead to photoreceptor cell death or neovascular complications. For this reason, it is interesting to study photoreceptor integrity in all stages of this disease. Besides being able to provide a structural description of the retinal layers thickness and integrity in three dimensions, functional additions to OCT such as angiography (OCTA) have been developed in recent years [9]. With the advent of OCTA, depth resolved microvascular imaging of the retina and choriocapillaris has been possible, becoming a powerful tool for clinical assessment of DR [10] as well as other retinal diseases [11,12,13]. Since structural OCT and OCTA can be acquired simultaneously, they are perfectly registered; OCT can be an important tool to investigate the anatomic relations between photoreceptor integrity and perfusion
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