FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912427512168448 |
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| author | Mu, Nan Song, Ruiqi Li, Xiaoning Xu, Zhihui Jiang, Jingfeng Zhao, Chen |
| author_facet | Mu, Nan Song, Ruiqi Li, Xiaoning Xu, Zhihui Jiang, Jingfeng Zhao, Chen |
| contents | Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of CAD. Methods: We propose the Frequency-Domain Attention-Guided Diffusion Network (FAD-Net), which integrates a frequency-domain-based attention mechanism and a cascading diffusion strategy to fully exploit frequency-domain information for improved segmentation accuracy. Specifically, FAD-Net employs a Multi-Level Self-Attention (MLSA) mechanism in the frequency domain, computing the similarity between queries and keys across high- and low-frequency components in ICAs. Furthermore, a Low-Frequency Diffusion Module (LFDM) is incorporated to decompose ICAs into low- and high-frequency components via multi-level wavelet transformation. Subsequently, it refines fine-grained arterial branches and edges by reintegrating high-frequency details via inverse fusion, enabling continuous enhancement of anatomical precision. Results and Conclusions: Extensive experiments demonstrate that FAD-Net achieves a mean Dice coefficient of 0.8717 in coronary artery segmentation, outperforming existing state-of-the-art methods. In addition, it attains a true positive rate of 0.6140 and a positive predictive value of 0.6398 in stenosis detection, underscoring its clinical applicability. These findings suggest that FAD-Net holds significant potential to assist in the accurate diagnosis and treatment planning of CAD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography Mu, Nan Song, Ruiqi Li, Xiaoning Xu, Zhihui Jiang, Jingfeng Zhao, Chen Image and Video Processing Computer Vision and Pattern Recognition Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of CAD. Methods: We propose the Frequency-Domain Attention-Guided Diffusion Network (FAD-Net), which integrates a frequency-domain-based attention mechanism and a cascading diffusion strategy to fully exploit frequency-domain information for improved segmentation accuracy. Specifically, FAD-Net employs a Multi-Level Self-Attention (MLSA) mechanism in the frequency domain, computing the similarity between queries and keys across high- and low-frequency components in ICAs. Furthermore, a Low-Frequency Diffusion Module (LFDM) is incorporated to decompose ICAs into low- and high-frequency components via multi-level wavelet transformation. Subsequently, it refines fine-grained arterial branches and edges by reintegrating high-frequency details via inverse fusion, enabling continuous enhancement of anatomical precision. Results and Conclusions: Extensive experiments demonstrate that FAD-Net achieves a mean Dice coefficient of 0.8717 in coronary artery segmentation, outperforming existing state-of-the-art methods. In addition, it attains a true positive rate of 0.6140 and a positive predictive value of 0.6398 in stenosis detection, underscoring its clinical applicability. These findings suggest that FAD-Net holds significant potential to assist in the accurate diagnosis and treatment planning of CAD. |
| title | FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.11454 |