# StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography

arXiv: https://arxiv.org/abs/2310.14961 · HTML: https://mlsanity.com/paper/2310.14961/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 1 of 1)

- [On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness](https://mlsanity.com/paper/2209.06078/) (2022)

## Built on by (most cited first, 8 of 8)

- [DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning](https://mlsanity.com/paper/2409.18340/) (2024)
- [Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection](https://mlsanity.com/paper/2508.00438/) (2025)
- [Brighteye: Glaucoma Screening with Color Fundus Photographs based on Vision Transformer](https://mlsanity.com/paper/2405.00857/) (2024)
- [Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning](https://mlsanity.com/paper/2411.11863/) (2024)
- [Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification](https://mlsanity.com/paper/2606.02605/) (2026)
- [ARIADNE: A Perception-Reasoning Synergy Framework for Trustworthy Coronary Angiography Analysis](https://mlsanity.com/paper/2603.19169/) (2026)
- [ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images](https://mlsanity.com/paper/2603.20021/) (2026)
- [SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos](https://mlsanity.com/paper/2609.27696/) (2026)

## Abstract (arXiv)

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.