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New AI models improve coronary artery segmentation for disease diagnosis · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers introduce advanced deep learning models for segmenting coronary arteries in Digital Subtraction Angiography (DSA) images. The first, HTC-SGA Former, utilizes a hybrid Transformer-CNN architecture with a novel Boundary-Weighted Adaptive Compound Loss (BWACL) to improve the segmentation of thin, low-contrast vessels and their boundaries. The second, MSA-UNet3+, employs a Multi-Scale Attention UNet3+ framework combined with a Supervised Prototypical Contrastive Loss (SPCL) to address class imbalance and enhance feature differentiation for more precise vessel delineation. Both methods demonstrate superior performance compared to existing state-of-the-art techniques on private datasets, offering more reliable analysis for cardiovascular interventions. AI

IMPACT These models offer improved accuracy in segmenting coronary arteries, potentially leading to more precise diagnosis and treatment planning for cardiovascular diseases.

RANK_REASON Two academic papers published on arXiv detailing new deep learning models for medical image segmentation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AI models improve coronary artery segmentation for disease diagnosis · 2 sources tracked

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Two academic papers published on arXiv detailing new deep learning models for medical image segmentation.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Rayan Merghani Ahmed, Marwa Omer Mohammed Omer, Mohamed Elmanna, Shijie Li, Bin Li, Shoujun Zhoua ·

    HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation

    arXiv:2606.29744v1 Announce Type: new Abstract: Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). However, thin low-contrast vessels, background interferen…

  2. arXiv cs.CV TIER_1 English(EN) · Rayan Merghani Ahmed, Adnan Iltaf, Mohamed Elmanna, Gang Zhao, Hongliang Li, Yue Du, Bin Li, Shoujun Zhou ·

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

    arXiv:2504.05184v4 Announce Type: replace-cross Abstract: 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 …

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