Adaptive Deep Clustering Network for Retinal Blood Vessel and Foveal Avascular Zone Segmentation

Abstract

Optical coherence tomography angiography (OCTA) is a new non-invasive imaging technology that provides detailed visual information on retinal biomarkers, such as the retinal vessel (RV) and the foveal avascular zone (FAZ). Ophthalmologists use these biomarkers to detect various retinal diseases, including diabetic retinopathy (DR) and hypertensive retinopathy (HR). However, only limited study is available on the parallel segmentation of RV and FAZ, due to multi-scale vessel complexity, inhomogeneous image quality, and non-perfusion, leading to erroneous segmentation. In this paper, we proposed a new adaptive segmented deep clustering (ASDC) approach that reduces features and boosts clustering performance by combining a deep encoder–decoder network with K-means clustering. This approach involves segmenting the image into RV and FAZ parts using separate encoder–decoder models and then employing K-means clustering on each part separated by the encoder–decoder models to obtain the final refined segmentation. To deal with the inefficiency of the encoder–decoder network during the down-sampling phase, we used separate encoding and decoding for each task instead of combining them into a single task. In summary, our method can segment RV and FAZ in parallel by reducing computational complexity, obtaining more accurate interpretable results, and providing an adaptive approach for a wide range of OCTA biomarkers. Our approach achieved 96% accuracy and can adapt to other biomarkers, unlike current segmentation methods that rely on complex networks for a single biomarker.