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StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography

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arxiv 2310.14961 v1 pith:NSUDFYJY submitted 2023-10-23 eess.IV cs.CVcs.LG

Hui Lin , Tom Liu , Aggelos Katsaggelos , Adrienne Kline This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords coronarystenosisangiographyx-rayalgorithmarcadearteryautomatic
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Coronary angiography continues to serve as the primary method for diagnosing coronary artery disease (CAD), which is the leading global cause of mortality. The severity of CAD is quantified by the location, degree of narrowing (stenosis), and number of arteries involved. In current practice, this quantification is performed manually using visual inspection and thus suffers from poor inter- and intra-rater reliability. The MICCAI grand challenge: Automatic Region-based Coronary Artery Disease diagnostics using the X-ray angiography imagEs (ARCADE) curated a dataset with stenosis annotations, with the goal of creating an automated stenosis detection algorithm. Using a combination of machine learning and other computer vision techniques, we propose the architecture and algorithm StenUNet to accurately detect stenosis from X-ray Coronary Angiography. Our submission to the ARCADE challenge placed 3rd among all teams. We achieved an F1 score of 0.5348 on the test set, 0.0005 lower than the 2nd place.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection

    eess.IV 2025-08 conditional novelty 6.0 of 10

    A user-guided diffusion inpainting pipeline synthesizes coronary angiograms with controlled stenosis severity, improving downstream lesion detection and severity classification over training on real data alone.

  2. Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models

    eess.IV 2024-12 conditional novelty 4.0 of 10

    On the public ARCADE coronary stenosis segmentation benchmark, the U-Mamba BOT model achieves an F1 score of 68.79%, the highest reported to date.

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