Segmentation of RBCC disruption and myopic stretch line in retinal OCT images using an improved U-shape network

Pathologic myopia (PM) is a major cause of legal blindness in the world. Linear lesions are closely related to PM, which include two types of lesions in the posterior fundus of pathologic eyes in optical coherence tomography (OCT) images: retinal pigment epithelium-Bruch's membrane-choriocapillaris complex (RBCC) disruption and myopic stretch line (MSL). In this paper, a fully automated method based on U-shape network is proposed to segment RBCC disruption and MSL in retinal OCT images. Compared with the original U-Net, there are two main improvements in the proposed network: (1) We creatively propose a new downsampling module named as feature aggregation pooling module (FAPM), which aggregates context information and local information. (2) Deep supervision module (DSM) is adopted to help the network converge faster and improve the segmentation performance. The proposed method was evaluated via 3-fold crossvalidation strategy on a dataset composed of 667 2D OCT B-scan images. The mean Dice similarity coefficient, Sensitivity and Jaccard of RBCC disruption and MSL are 0.626, 0.665, 0.491 and 0.739, 0.814, 0.626, respectively. The primary experimental results show the effectiveness of our proposed method.