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CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram

arXiv – CS AI|Alvee Hassan, Rusab Sarmun, Muhammad E. H. Chowdhury, M Murugappan, Abdulrahman Alqahtani, Balamurugan Balusamy, Sohaib Bassam Zoghoul|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed CASR-Net, a deep learning pipeline for automated coronary artery segmentation in X-ray angiograms that combines image preprocessing, UNet-based segmentation, and refinement stages. The system achieved superior performance with 61.43% IoU and 76.10% DSC on public datasets, potentially improving clinical diagnosis of coronary artery disease.

Key Takeaways

→CASR-Net introduces a three-stage pipeline combining novel multichannel preprocessing, segmentation, and refinement for coronary artery analysis.
→The system uses a UNet architecture with DenseNet121 encoder and Self-ONN decoder to preserve continuity of narrow vessel branches.
→Novel preprocessing strategy combining CLAHE and improved Ben Graham method provides incremental performance gains.
→Achieved state-of-the-art results with 61.43% IoU, 76.10% DSC, and 79.36% clDice on combined public datasets.
→The automated approach offers valuable clinical support for coronary artery disease diagnosis and treatment planning.

#medical-ai #deep-learning #image-processing #healthcare #computer-vision #coronary-artery #segmentation #clinical-ai

Read Original →via arXiv – CS AI

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