MA‐ResUNet: Multi‐attention optic cup and optic disc segmentation based on improved U‐Net-Reference-Cited by-同舟云学术

MA‐ResUNet: Multi‐attention optic cup and optic disc segmentation based on improved U‐Net

Published:2024-06-17 Issue: Volume: Page:
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Zhang Xiaoqian¹²^ORCID,Lin Ying²³,Li Linxuan²³,Zeng Jingyu²⁴,Lan Xianmei²³,Zhang Xinyi²³,Jia Yongjian²,Tao Ye²,Wang Lin²,Wang Yu²,Li Yu²,Zong Yang²,Jin Xin²⁵,Liu Panhong²,Cheng Xinyu¹,Zhu Huanhuan²

Affiliation:

1. Engineering Research Center of Text Computing and Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology Guizhou University Guiyang China

2. Precision Health Institute BGI Research Shenzhen China

3. College of Life Sciences University of Chinese Academy of Sciences Beijing China

4. College of Life Sciences Northwest A&F University Yangling Shaanxi China

5. School of Medicine South China University of Technology Guangzhou China

Abstract

AbstractGlaucoma poses a significant threat to vision, capable of causing irreversible damage and, in severe instances, leading to permanent blindness. Accurate optic cup (OC) and optic disc (OD) segmentation are essential in glaucoma screening. In this study, a novel OC and OD segmentation approach is proposed. Based on U‐Net, it is optimized by introducing cardinality dimensions. Moreover, attention gates are implemented to reinforce salient features while suppressing irrelevant information. Additionally, a convolutional block attention module (CBAM) is integrated into the decoder segment. This fusion hones in on effective information in both channel and spatial dimensions. Meanwhile, an image processing procedure is proposed for image normalization and enhancement. All of these increase the accuracy of the model. This model is evaluated on the ORIGA and REFUGE datasets, demonstrating the model's superiority in OC and OD segmentation compared to the state‐of‐the‐art methods. Additionally, after the proposed image processing, cup‐to‐disc ratio (CDR) prediction on a batch of 155 in‐house fundus images yields an absolute CDR error of 0.099, which is reduced by 0.04 compared to the case where only conventional processing was performed.

Publisher

Institution of Engineering and Technology (IET)

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