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Regular Paper

PCRTAM-Net: A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin 541004, China
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
Department of Pathology, Ganzhou Municipal Hospital, Ganzhou 341000, China

Idowu Paul Okuwobi worked on the investigation and writing and provided computing power, and Bing-Bing Li was responsible for the data curation and methodology.

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Abstract

Retinal images play an essential role in the early diagnosis of ophthalmic diseases. Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background. At the same time, automated models struggle to capture representative and discriminative retinal vascular features. To fully utilize the structural information of the retinal blood vessels, we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network (PCRTAM-Net). PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network. In addition, the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow. A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies. We evaluate the proposed PCRTAM-Net on four publicly available datasets, DRIVE, CHASE_DB1, STARE, and HRF. Our model achieves state-of-the-art performance of 97.10%, 97.70%, 97.68%, and 97.14% for ACC and 83.05%, 82.26%, 84.64%, and 81.16% for F1, respectively.

Keywords

residual networkretinal image segmentationtriple attention mechanismatrous convolution

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Journal of Computer Science and Technology
Volume 38 Issue 3,
May 2023
Pages 567-581
DOI: 10.1007/s11390-023-3066-4
Cite this article:
Wang H-D, Li Z-Z, Okuwobi IP, et al. PCRTAM-Net: A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation. Journal of Computer Science and Technology, 2023, 38(3): 567-581. https://doi.org/10.1007/s11390-023-3066-4

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Received: 30 December 2022
Accepted: 22 May 2023
Published: 30 May 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023
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