Segmentation of retinal detachment and retinoschisis in OCT images based on improved U-shaped network with cross-fusion global feature module

Changqing Yang; Xinjian Chen; Jinzhu Su; Weifang Zhu; Qiuying Chen; Jiayi Yu; Ying Fan; Fei Shi

doi:10.1117/12.2580665

15 February 2021 Segmentation of retinal detachment and retinoschisis in OCT images based on improved U-shaped network with cross-fusion global feature module

Changqing Yang, Xinjian Chen, Jinzhu Su, Weifang Zhu, Qiuying Chen, Jiayi Yu, Ying Fan, Fei Shi

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159621 (2021) https://doi.org/10.1117/12.2580665
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Retinal detachment (RD) refers to the separation of the retinal neuroepithelium layer (RNE) and retinal pigment epithelium (RPE), and retinoschisis (RS) is characterized by the RNE splitting into multiple layers. Retinal detachment and retinoschisis are the main complications leading to vision loss in high myopia. Optical coherence tomography (OCT) is the main imaging method for observing retinal detachment and retinoschisis. This paper proposes a U-shaped convolutional neural network with a cross-fusion global feature module (CFCNN) to achieve automatic segmentation of retinal detachment and retinoschisis. Main contributions include: (1) A new cross-fusion global feature module (CFGF) is proposed. (2) The residual block is integrated into the encoder of the U-Net network to enhance the extraction of semantic information. The method was tested on a dataset consisting of 540 OCT B-scans. With the proposed CFCNN method, the mean Dice similarity coefficient of retinal detachment and retinoschisis segmentation reached 94.33% and 90.29% and were better than some existing advanced segmentation networks.

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Changqing Yang, Xinjian Chen, Jinzhu Su, Weifang Zhu, Qiuying Chen, Jiayi Yu, Ying Fan, and Fei Shi "Segmentation of retinal detachment and retinoschisis in OCT images based on improved U-shaped network with cross-fusion global feature module", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159621 (15 February 2021); https://doi.org/10.1117/12.2580665

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