Evidence map›Paper›PMID 41514152›Full record

ArticleAnnals of biomedical engineering2026

Design Rules for Robust Coronary Artery Segmentation: A Systematic Analysis of Dataset Size, Windowing, Architectures, and Vessel Geometry.

Ming-Hui Hung, Yu-Wei Chiang, Hao-Yun Liu, Han-Hsuan Chu, Yu-Sen Huang, Pin-Yi Chiang, Hsin-Ming Chen, Tzung-Dau Wang, Shyh-Jye Chen, Wen-Jeng Lee

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Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

10 authors.

Ming-Hui HungDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Yu-Wei ChiangDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Hao-Yun LiuDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Han-Hsuan ChuDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Yu-Sen HuangDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Pin-Yi ChiangDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Hsin-Ming ChenDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Tzung-Dau WangDivision of Cardiology, Department of Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
Shyh-Jye ChenDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan.
Wen-Jeng LeeDepartment of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, 7 Chung-Shan South Road, Taipei, 100, Taiwan. wenjeng@gmail.com.ORCID http://orcid.org/0000-0003-3267-4811

Funding

National Science and Technology Council NSTC 113-2314-B-002-161-MY3National Science and Technology Council NSTC 114-2221-E-002-207
6 · The paper itself

Abstract

purposeAutomated coronary artery segmentation from computed tomography coronary angiography (CTCA) can support quantitative analysis, yet performance varies with data characteristics and model configuration. This study systematically evaluates how dataset size, computed tomography (CT) window width and level (W/L) values, model architectures, and vessel geometry affect nnU-Net-based coronary artery segmentation.

methodsUsing 1000 annotated CTCA scans from the publicly available ImageCAS dataset, we conducted a series of experiments in which we varied the number of training cases, applied different W/L values, compared 2D and multiple 3D nnU-Net architectures (Full-Resolution, Low-Resolution, Cascade, and ensemble), and assessed performance across vessel curvature and tortuosity. All models were evaluated on an independent 200-case test set using Dice similarity coefficient (DSC) and intersection over union (IoU).

resultsSegmentation performance increased with dataset size and plateaued beyond 100 training cases. W/L values of 800/200 Hounsfield units (HU) and 1300/350 HU slightly improved performance over original images. 3D models outperformed the 2D model, with the 3D ensemble achieving the highest accuracy (DSC 0.8337; 95% confidence interval [CI]: 0.8269-0.8405, IoU 0.7178; 95% CI: 0.7080-0.7275). Performance remained consistent across vessels with different curvature and tortuosity, indicating model robustness to geometric complexity.

conclusionThe nnU-Net framework achieves accurate and generalizable coronary artery segmentation across diverse dataset sizes, W/L values, model architectures, and vessel geometries. Optimizing input settings and architectural strategies further enhances segmentation accuracy.

Indexed as

Computed Tomography AngiographyCoronary AngiographyCoronary VesselsHumansArchitectureComputed tomography angiographyCoronary artery segmentationDataset sizeVessel geometryWindowing

Identifiers

PMID41514152

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