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 the BRAO region in the retina is necessary for the assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to segment BRAO regions 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, the input 3D image is automatically classified into BRAO of acute phase and BRAO of chronic phase or normal retina using an AdaBoost classifier based on combining local structural, intensity, textural features with our new feature distribution analyzing strategy. Then, BRAO regions of acute phase and chronic phase are segmented separately. A thickness model is built to segment BRAO in the chronic phase. While for segmenting BRAO in the acute phase, a two-step segmentation strategy is performed: rough initialization and refine segmentation. The proposed method was tested on SD-OCT images of 35 patients (12 BRAO acute phase, 11 BRAO chronic phase, and 12 normal eyes) using the leave-one-out strategy. The classification accuracy for BRAO acute phase, BRAO chronic phase, and normal retina were 100%, 90.9%, and 91.7%, respectively. The overall true positive volume fraction (TPVF) and false positive volume fraction (FPVF) for the acute phase were 91.1% and 5.5% and for the chronic phase were 92.7% and 8.4%, respectively.