DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation

Yongchao Duan, Rui Yang (), Ming Zhao, Mingrui Qi and Sheng-Lung Peng
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Yongchao Duan: School of Atmosphere and Remote Sensing, Wuxi University, Wuxi 214105, China
Rui Yang: School of Computer Science, Yangtze University, Jingzhou 434025, China
Ming Zhao: School of Computer Science, Yangtze University, Jingzhou 434025, China
Mingrui Qi: School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
Sheng-Lung Peng: Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei 10051, China

Mathematics, 2025, vol. 13, issue 9, 1-17

Abstract: Segmentation of retinal vessels from fundus images is critical for diagnosing diseases such as diabetes and hypertension. However, the inherent challenges posed by the complex geometries of vessels and the highly imbalanced distribution of thick versus thin vessel pixels demand innovative solutions for robust feature extraction. In this paper, we introduce DAF-UNet, a novel architecture that integrates advanced modules to address these challenges. Specifically, our method leverages a pre-trained deformable convolution (DC) module within the encoder to dynamically adjust the sampling positions of the convolution kernel, thereby adapting the receptive field to capture irregular vessel morphologies more effectively than traditional convolutional approaches. At the network’s bottleneck, an enhanced atrous spatial pyramid pooling (ASPP) module is employed to extract and fuse rich, multi-scale contextual information, significantly improving the model’s capacity to delineate vessels of varying calibers. Furthermore, we propose a hybrid loss function that combines pixel-level and segment-level losses to robustly address the segmentation inconsistencies caused by the disparity in vessel thickness. Experimental evaluations on the DRIVE and CHASE_DB1 datasets demonstrated that DAF-UNet achieved a global accuracy of 0.9572/0.9632 and a Dice score of 0.8298/0.8227, respectively, outperforming state-of-the-art methods. These results underscore the efficacy of our approach in precisely capturing fine vascular details and complex boundaries, marking a significant advancement in retinal vessel segmentation.

Keywords: retinal vessel segmentation; deformable convolution; contextual feature fusion; U-Net architecture (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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