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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||2 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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