Group-wise context selection network for choroid segmentation in optical coherence tomography. (9th December 2021)

• Record Type: Journal Article
• Authors: Shi, Fei; Cheng, Xuena; Feng, Shuanglang; Yang, Changqing; Diao, Shengyong; Zhu, Weifang; Xiang, Dehui; Chen, Qiuying; Xu, Xun; Chen, Xinjian; Fan, Ying
• Is Part Of: Physics in medicine & biology. Volume 66:Number 24(2021)
• Publication Date: 2021-12-09
• Subjects: choroid segmentation -- optical coherence tomography -- channel attention -- spatial attention -- deep learning; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac3a23
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Choroid thickness measured from optical coherence tomography (OCT) images has emerged as a vital metric in the management of retinal diseases such as high myopia. In this paper, we propose a novel group-wise context selection network (referred to as GCS-Net) to segment the choroid of either normal or high myopia eyes. To deal with the diverse choroid thickness and the variable shape of the pathological retina, GCS-Net adopts the group-wise channel dilation (GCD) module and the group-wise spatial dilation module, which can automatically select group-wise multi-scale information under the guidance of channel attention or spatial attention, and enhance the consistency between the receptive field and the target area. Furthermore, a boundary optimization network with a new edge loss is incorporated to improve the resulting choroid boundary by deep supervision. Experimental results evaluated on a dataset composed of 1650 clinically obtained OCT B-scans show that the proposed GCS-Net can achieve a Dice similarity coefficient of 95.97 ± 0.54%, which outperforms some state-of-the-art segmentation networks.