Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images

Abstract

We propose a pipeline for automatically segmenting cortex and nucleus in a 360-degree anterior segment optical coherence tomography (AS-OCT) image. The proposed pipeline consists of a U-shaped network followed by a shape template. The U-shaped network predicts a mask for cortex and nucleus. However, the boundary between cortex and nucleus is weak, so that the boundary of the prediction is an irregular shape and does not satisfy the physiological structure of nucleus. To address this problem, in the second step, we design a shape template according to the physiological structure of nucleus to refine the boundary. Our method integrates both appearance and structure information. The accuracy is measured by the normalized mean squared error (NMSE) between ground truth line and predicted line. We achieve NMSE 7.09/7.94 for nucleus top/bottom boundary and 2.49/2.43 for cortex top/bottom boundary.