[2510.27315] CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram

Computer Science > Computer Vision and Pattern Recognition arXiv:2510.27315 (cs) [Submitted on 31 Oct 2025 (v1), last revised 3 Mar 2026 (this version, v2)] Title:CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram Authors:Alvee Hassan, Rusab Sarmun, Muhammad E. H. Chowdhury, M Murugappan, Abdulrahman Alqahtani, Balamurugan Balusamy, Sohaib Bassam Zoghoul View a PDF of the paper titled CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram, by Alvee Hassan and 6 other authors View PDF Abstract:Early detection of coronary artery disease (CAD) is critical for reducing mortality and improving patient treatment planning. While angiographic image analysis from X-rays is a common and cost-effective method for identifying cardiac abnormalities, including stenotic coronary arteries, poor image quality can significantly impede clinical diagnosis. We present the Coronary Artery Segmentation and Refinement Network (CASR-Net), a three-stage pipeline comprising image preprocessing, segmentation, and refinement. A novel multichannel preprocessing strategy combining CLAHE and an improved Ben Graham method provides incremental gains, increasing Dice Score Coefficient (DSC) by 0.31-0.89% and Intersection over Union (IoU) by 0.40-1.16% compared with using the techniques individually. The core innovation is a segmentation n...