A Framework for Classification and Segmentation of Branch Retinal Artery Occlusion in SD-OCT

Branch retinal artery occlusion (BRAO) is an ocular emergency which could lead to blindness. Quantitative analysis of BRAO region in the retina is very needed to assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to classify and segment BRAO based on 3D spectral-domain optical coherence tomography (SD-OCT) images. To the best of our knowledge, this is the first automatic 3D BRAO segmentation framework. First, a support vector machine (SVM) based classifier is designed to differentiate BRAO into acute phase and chronic phase, and the two types are segmented separately. To segment BRAO in chronic phase, a threshold-based method is proposed based on the thickness of inner retina. While for segmenting BRAO in acute phase, a two-step segmentation is performed, which includes the bayesian posterior probability based initialization and the graph-search-graph-cut based segmentation. The proposed method was tested on SD-OCT images of 23 patients (12 of acute and 11 of chronic phase) using leave-one-out strategy. The overall classification accuracy of SVM classifier was 87.0%, and the TPVF and FPVF for acute phase were 91.1%, 5.5%; for chronic phase were 90.5%, 8.7%, respectively.