DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/9/1454 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850137667568664576 |
|---|---|
| author | Yongchao Duan Rui Yang Ming Zhao Mingrui Qi Sheng-Lung Peng |
| author_facet | Yongchao Duan Rui Yang Ming Zhao Mingrui Qi Sheng-Lung Peng |
| author_sort | Yongchao Duan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-00109efdd9174edabcf2fae42b6db031 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-00109efdd9174edabcf2fae42b6db0312025-08-20T02:30:46ZengMDPI AGMathematics2227-73902025-04-01139145410.3390/math13091454DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel SegmentationYongchao Duan0Rui Yang1Ming Zhao2Mingrui Qi3Sheng-Lung Peng4School of Atmosphere and Remote Sensing, Wuxi University, Wuxi 214105, ChinaSchool of Computer Science, Yangtze University, Jingzhou 434025, ChinaSchool of Computer Science, Yangtze University, Jingzhou 434025, ChinaSchool of Internet of Things Engineering, Wuxi University, Wuxi 214105, ChinaDepartment of Creative Technologies and Product Design, National Taipei University of Business, Taipei 10051, ChinaSegmentation 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.https://www.mdpi.com/2227-7390/13/9/1454retinal vessel segmentationdeformable convolutioncontextual feature fusionU-Net architecture |
| spellingShingle | Yongchao Duan Rui Yang Ming Zhao Mingrui Qi Sheng-Lung Peng DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation Mathematics retinal vessel segmentation deformable convolution contextual feature fusion U-Net architecture |
| title | DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation |
| title_full | DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation |
| title_fullStr | DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation |
| title_full_unstemmed | DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation |
| title_short | DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation |
| title_sort | daf unet deformable u net with atrous convolution feature pyramid for retinal vessel segmentation |
| topic | retinal vessel segmentation deformable convolution contextual feature fusion U-Net architecture |
| url | https://www.mdpi.com/2227-7390/13/9/1454 |
| work_keys_str_mv | AT yongchaoduan dafunetdeformableunetwithatrousconvolutionfeaturepyramidforretinalvesselsegmentation AT ruiyang dafunetdeformableunetwithatrousconvolutionfeaturepyramidforretinalvesselsegmentation AT mingzhao dafunetdeformableunetwithatrousconvolutionfeaturepyramidforretinalvesselsegmentation AT mingruiqi dafunetdeformableunetwithatrousconvolutionfeaturepyramidforretinalvesselsegmentation AT shenglungpeng dafunetdeformableunetwithatrousconvolutionfeaturepyramidforretinalvesselsegmentation |