Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 3
943-948

Based on U-Net multi-scale self-calibrating attention retinal segmentation algorithm

Liang Liming
Chen Xin
Zhou Longsong
Yu Jie
School of Electrical Engineering & Automation, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0317

Abstract

Aiming at the problem of the low accuracy of fine retinal vessel segmentation, this paper proposed improved U-Net methods combining scalable cascade modules, Transformer and self-calibrated attention modules to improve the accuracy of fine retinal vessel segmentation. Firstly, this paper used scalable cascaded modules in the encoding stage to enable the learning of complex and variable retinal vessel structures. Secondly, in the decoding stage, this paper proposed a self-calibration attention mechanism, which used the multi-scale compression excitation module to adaptively recalibrate the blood vessel from channel and spatial of features, it could enhance the feature response of the target area, and suppressed the background noise. Finally, the Transformer feature extraction block improved the capability of feature space mapping. This proposed method tested on public datasets, i. e. the DRIVE and CHASEDB1. The experimental results of proposed method show that the accuracy of the retinal vessel segmentation from the two datasets reach 96.49%/96.67%, the sensitivity reach 83.75%/83.30%, the specificity reach 98.28%/98.01% and the AUC reach 0.987 1/0.987 2, respectively. The performance of proposed method is better than most of existing methods, and each modules can improve the ability of fine retinal vessel segmentation.

Foundation Support

国家自然科学基金资助项目(51365017,61463018)
江西省自然科学基金资助项目(20192BAB205084)
江西省教育厅科学技术研究重点项目(GJJ170491)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0317
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 3
Section: Technology of Graphic & Image
Pages: 943-948
Serial Number: 1001-3695(2023)03-050-0943-06

Publish History

[2022-09-22] Accepted Paper
[2023-03-05] Printed Article

Cite This Article

梁礼明, 陈鑫, 周珑颂, 等. 基于U-Net多尺度自校准注意力视网膜分割算法 [J]. 计算机应用研究, 2023, 40 (3): 943-948. (Liang Liming, Chen Xin, Zhou Longsong, et al. Based on U-Net multi-scale self-calibrating attention retinal segmentation algorithm [J]. Application Research of Computers, 2023, 40 (3): 943-948. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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