Joint optic disc and cup segmentation using feature fusion and attention. (November 2022)

• Record Type: Journal Article
• Authors: Guo, Xiaoxin; Li, Jiahui; Lin, Qifeng; Tu, Zhenchuan; Hu, Xiaoying; Che, Songtian
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Deep learning -- Glaucoma screening -- OD and OC segmentation -- U-Net -- Attention; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106094
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Currently, glaucoma is one of the leading causes of irreversible vision loss. So far, glaucoma is incurable, but early treatment can stop the progression of the condition and slow down the speed and extent of vision loss. Early detection and treatment are crucial to prevent glaucoma from developing into blindness. It is an effective method for glaucoma diagnosis to measure Cup to Disc Ratio (CDR) by the segmentation of Optic Disc (OD) and Optic Cup (OC). Compared with OD segmentation, OC segmentation still faces difficulties in segmentation accuracy. In this paper, a deep learning architecture named FAU-Net (feature fusion and attention U-Net) is proposed for the joint segmentation of OD and OC. It is an improved architecture based on U-Net. By adding a feature fusion module in U-Net, information loss in feature extraction can be reduced. The channel and spatial attention mechanisms are combined to highlight the important features related to the segmentation task and suppress the expression of irrelevant regional features. Finally, a multi-label loss is used to generate the final joint segmentation of OD and OC. Experimental results show that the proposed FAU-Net outperforms the state-of-the-art segmentation of OD and OC on Drishti-GS1, REFUGE, RIM-ONE and ODIR datasets. Highlights: The FAU-Net is proposed for joint OC/OD segmentation. The feature fusion module is proposed to preserve image details. The CSAMs are proposed to highlight the effect of channel and spatial dimension. The effectiveness and generalization of the FAU-Net are evaluated and verified.