A Multi-task Framework for Topology-guaranteed Retinal Layer Segmentation in OCT Images

Abstract

Optical coherence tomography (OCT) imaging can obtain high-resolution cross-sectional scans of the retina, which can be used in clinical diagnosis. Changes in the thickness of layers indicate the onset of retinal diseases, motivating an accurate measurement of the thickness of retinal layers. Thus, an automatic and robust layer segmentation method is necessary. In this paper, we propose a deep learning-based multi-task framework to obtain the topologically consistent layer segmentation in OCT B-scans. By integrating the distance maps of retinal layer surfaces, the segmentation task is regarded as a multi-task problem of regression and classification. Besides, considering the multi-task learning problem, we propose a task-specific attention module to learn the task-tailored features. Experiment results on a public OCT dataset with multiple sclerosis (MS) demonstrate the effectiveness of the proposed method.