SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model
Coronary artery disease (CAD) is a highly prevalent cardiovascular disease and one of the leading causes of death worldwide. The accurate segmentation of coronary arteries from CT angiography (CTA) images is essential for the diagnosis and treatment of coronary artery disease. However, due to small...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/11/6/192 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849432775649656832 |
|---|---|
| author | Ruoxuan Xu Longhui Dai Jianru Wang Lei Zhang Yuanquan Wang |
| author_facet | Ruoxuan Xu Longhui Dai Jianru Wang Lei Zhang Yuanquan Wang |
| author_sort | Ruoxuan Xu |
| collection | DOAJ |
| description | Coronary artery disease (CAD) is a highly prevalent cardiovascular disease and one of the leading causes of death worldwide. The accurate segmentation of coronary arteries from CT angiography (CTA) images is essential for the diagnosis and treatment of coronary artery disease. However, due to small vessel diameters, large morphological variations, low contrast, and motion artifacts, conventional segmentation methods, including classical image processing (such as region growing and level sets) and early deep learning models with limited receptive fields, are unsatisfactory. We propose SADiff, a hybrid framework that integrates a dilated attention network (DAN) for ROI extraction, a diffusion-based subnet for noise suppression in low-contrast regions, and a striped attention network (SAN) to refine tubular structures affected by morphological variations. Experiments on the public ImageCAS dataset show that it has a Dice score of 83.48% and a Hausdorff distance of 19.43 mm, which is 6.57% higher than U-Net3D in terms of Dice. The cross-dataset validation on the private ImageLaPP dataset verifies its generalizability with a Dice score of 79.42%. This comprehensive evaluation demonstrates that SADiff provides a more efficient and versatile method for coronary segmentation and shows great potential for improving the diagnosis and treatment of CAD. |
| format | Article |
| id | doaj-art-20d9fd6fdb2a4e8bbc8b4bee245795a0 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-20d9fd6fdb2a4e8bbc8b4bee245795a02025-08-20T03:27:17ZengMDPI AGJournal of Imaging2313-433X2025-06-0111619210.3390/jimaging11060192SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion ModelRuoxuan Xu0Longhui Dai1Jianru Wang2Lei Zhang3Yuanquan Wang4School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, ChinaSchool of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, ChinaSchool of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, ChinaSchool of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, ChinaSchool of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, ChinaCoronary artery disease (CAD) is a highly prevalent cardiovascular disease and one of the leading causes of death worldwide. The accurate segmentation of coronary arteries from CT angiography (CTA) images is essential for the diagnosis and treatment of coronary artery disease. However, due to small vessel diameters, large morphological variations, low contrast, and motion artifacts, conventional segmentation methods, including classical image processing (such as region growing and level sets) and early deep learning models with limited receptive fields, are unsatisfactory. We propose SADiff, a hybrid framework that integrates a dilated attention network (DAN) for ROI extraction, a diffusion-based subnet for noise suppression in low-contrast regions, and a striped attention network (SAN) to refine tubular structures affected by morphological variations. Experiments on the public ImageCAS dataset show that it has a Dice score of 83.48% and a Hausdorff distance of 19.43 mm, which is 6.57% higher than U-Net3D in terms of Dice. The cross-dataset validation on the private ImageLaPP dataset verifies its generalizability with a Dice score of 79.42%. This comprehensive evaluation demonstrates that SADiff provides a more efficient and versatile method for coronary segmentation and shows great potential for improving the diagnosis and treatment of CAD.https://www.mdpi.com/2313-433X/11/6/192coronary arteryimage segmentationdiffusion modelmulti-scale spatial attention |
| spellingShingle | Ruoxuan Xu Longhui Dai Jianru Wang Lei Zhang Yuanquan Wang SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model Journal of Imaging coronary artery image segmentation diffusion model multi-scale spatial attention |
| title | SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model |
| title_full | SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model |
| title_fullStr | SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model |
| title_full_unstemmed | SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model |
| title_short | SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model |
| title_sort | sadiff coronary artery segmentation in ct angiography using spatial attention and diffusion model |
| topic | coronary artery image segmentation diffusion model multi-scale spatial attention |
| url | https://www.mdpi.com/2313-433X/11/6/192 |
| work_keys_str_mv | AT ruoxuanxu sadiffcoronaryarterysegmentationinctangiographyusingspatialattentionanddiffusionmodel AT longhuidai sadiffcoronaryarterysegmentationinctangiographyusingspatialattentionanddiffusionmodel AT jianruwang sadiffcoronaryarterysegmentationinctangiographyusingspatialattentionanddiffusionmodel AT leizhang sadiffcoronaryarterysegmentationinctangiographyusingspatialattentionanddiffusionmodel AT yuanquanwang sadiffcoronaryarterysegmentationinctangiographyusingspatialattentionanddiffusionmodel |