Cu-Segnet: Corneal Ulcer Segmentation Network

Abstract

Corneal ulcer is a common-occurring illness in cornea. It is a challenge to segment corneal ulcer in slit-lamp image due to the different sizes and shapes of point-flaky mixed corneal ulcer and flaky corneal ulcer. These differences introduce inconsistency and effect the prediction accuracy. To address this problem, we propose a corneal ulcer segmentation network (CU-SegNet) to segment corneal ulcer in fluorescein staining image. In CU-SegNet, the encoder-decoder structure is adopted as main framework, and two novel modules including multi-scale global pyramid feature aggregation (MGPA) module and multi-scale adaptive-aware deformation (MAD) module are proposed and embedded into the skip connection and the top of encoder path, respectively. MGPA helps high-level features supplement local high-resolution semantic information, while MAD can guide the network to focus on multi-scale deformation features and adaptively aggregate contextual information. The proposed network is evaluated on the public SUSTech-SYSU dataset. The Dice coefficient of the proposed method is 89.14%.