Coronary Angiography

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Coronary angiography research develops ways to interpret X-ray images and videos of the heart’s arteries, where motion, overlapping vessels, and variable image quality make assessment difficult. Common approaches include U-Net and transformer models for vessel segmentation, video models for stenosis and disease scoring, and multi-view or physics-informed methods for reconstructing 3D anatomy and estimating blood flow. Researchers also use synthetic data, self-supervised and cross-modal learning, and vision-language models to reduce annotation needs and support structured reporting or catheter guidance. A central challenge is ensuring reliable performance across hospitals and acquisition conditions, with external validation and clinical testing needed to establish whether automated measurements improve care.

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31 papers4 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation
Apr 7, 2025Rayan Merghani Ahmed, Adnan Iltaf, Mohamed Elmanna +5Contrastive Learning Medical Imaging
Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China · University of Chinese Academy of Sciences (UCAS), Beijing, 101408, China · Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Cairo, 12613, Egypt +2

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MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation

Rayan Merghani Ahmed, Adnan Iltaf, Mohamed Elmanna, Gang Zhao+4

Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China · University of Chinese Academy of Sciences (UCAS), Beijing, 101408, China · Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Cairo, 12613, Egypt+2

Contrastive Learning Medical Imaging Coronary Angiography Multi-Scale Feature Fusion Medical Image Segmentation Coronary Artery Segmentation+1

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Accurate segmentation of coronary Digital Subtraction Angiography (DSA) images is essential for diagnosing and treating coronary artery disease (CAD). Despite advances in deep learning, challenges such as high intra-class variance and class imbalance limit precise vessel delineation. Existing approaches for coronary DSA segmentation cannot effectively address these issues. Furthermore, existing segmentation network encoders do not directly generate semantic embeddings, which could enable the decoder to reconstruct segmentation masks more effectively. We propose a Supervised Prototypical Contrastive Loss (SPCL) that combines supervised and prototypical contrastive learning to enhance coronary DSA image segmentation. The supervised contrastive loss enforces semantic embeddings in the encoder, improving feature differentiation. The prototypical contrastive loss enables the model to focus on the foreground class while alleviating high intra-class variance and class imbalance by concentrating only on hard-to-classify background samples. We implement the proposed SPCL within MSA-UNet3+, a Multi-Scale Attention-Enhanced UNet3+ architecture. The architecture integrates a Multi-Scale Attention Encoder (M-encoder), a Multi-Scale Dilated Bottleneck (MSD-Bottleneck) for multi-scale feature extraction, and a Contextual Attention Fusion Module (CAFM) to preserve fine-grained details while improving contextual understanding. Experiments on a private coronary DSA dataset demonstrate that MSA-UNet3+ outperforms state-of-the-art methods, achieving the highest Dice coefficient and F1-score while significantly reducing ASD and ACD. The framework provides precise vessel segmentation for accurate identification of coronary stenosis and supports informed diagnostic and therapeutic decisions. The code will be released at https://github.com/rayanmerghani/MSA-UNet3plus.