Choroid segmentation in OCT images based on improved U-net

Change of the thickness and volume of the choroid, which can be observed and quantified from optical coherence tomography (OCT) images, is a feature of many retinal diseases, such as aged-related macular degeneration and myopic maculopathy. In this paper, we make purposeful improvements on the U-net for segmenting the choroid of either normal or pathological myopia retina, obtaining the Bruch’s membrane (BM) and the choroidal-scleral interface (CSI). There are two main improvements to the U-net framework: (1) Adding a refinement residual block (RRB) to the back of each encoder. This strengthens the recognition ability of each stage; (2) The channel attention block (CAB) is integrated with the U-net. This enables high-level semantic information to guide the underlying details and handle the intra-class inconsistency problem. We validated our improved network on a dataset which consists of 952 OCT Bscans obtained from 95 eyes from both normal subjects and patients suffering from pathological myopia. Comparing with manual segmentation, the mean choroid thickness difference is 8μm, and the mean Dice similarity coefficient is 85.0%.