Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Yousefzadeh, Mehdi Barough, Siavash Shirzadeh Fakharifar, Ashkan Tayyarazad, Yashar Eghbali, Narges Mozaffari, Mohaddeseh Taeb, Hoda Tabatabaee, Negar Sadat Rafiee Esfahanian, Parsa Gohar, Ghazaleh Sadeghi Safavirad, Amineh Mazloomzadeh, Saeideh khalilipur, Ehsan Elahifar, Armin Maleki, Majid |
| author_facet | Yousefzadeh, Mehdi Barough, Siavash Shirzadeh Fakharifar, Ashkan Tayyarazad, Yashar Eghbali, Narges Mozaffari, Mohaddeseh Taeb, Hoda Tabatabaee, Negar Sadat Rafiee Esfahanian, Parsa Gohar, Ghazaleh Sadeghi Safavirad, Amineh Mazloomzadeh, Saeideh khalilipur, Ehsan Elahifar, Armin Maleki, Majid |
| contents | X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17429 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography Yousefzadeh, Mehdi Barough, Siavash Shirzadeh Fakharifar, Ashkan Tayyarazad, Yashar Eghbali, Narges Mozaffari, Mohaddeseh Taeb, Hoda Tabatabaee, Negar Sadat Rafiee Esfahanian, Parsa Gohar, Ghazaleh Sadeghi Safavirad, Amineh Mazloomzadeh, Saeideh khalilipur, Ehsan Elahifar, Armin Maleki, Majid Computer Vision and Pattern Recognition Artificial Intelligence X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation. |
| title | Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.17429 |