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

2025-10-31

AI Summary

This study addresses low segmentation accuracy, fragmented small vascular branches, and high false-positive rates in X-ray coronary angiography images. We propose a three-stage deep learning framework: (1) multi-channel preprocessing integrating CLAHE with an improved Ben Graham method to enhance contrast and suppress noise; (2) a backbone segmentation network leveraging a DenseNet121 encoder and a Self-ONN decoder to strengthen feature representation; and (3) a contour refinement module embedded to improve vascular boundary continuity and topological consistency. Evaluated via five-fold cross-validation on two public datasets, our method achieves IoU = 61.43%, Dice Similarity Coefficient (DSC) = 76.10%, and clDice = 79.36%, outperforming state-of-the-art models. The framework delivers robust, clinically relevant vessel segmentation, thereby supporting early diagnosis and precise treatment planning for coronary artery disease.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

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 network built on a UNet with a DenseNet121 encoder and a Self-organized Operational Neural Network (Self-ONN) based decoder, which preserves the continuity of narrow and stenotic vessel branches. A final contour refinement module further suppresses false positives. Evaluated with 5-fold cross-validation on a combination of two public datasets that contain both healthy and stenotic arteries, CASR-Net outperformed several state-of-the-art models, achieving an IoU of 61.43%, a DSC of 76.10%, and clDice of 79.36%. These results highlight a robust approach to automated coronary artery segmentation, offering a valuable tool to support clinicians in diagnosis and treatment planning.

Problem

• Automating coronary artery segmentation in X-ray angiograms
• Improving segmentation accuracy for narrow and stenotic vessels
• Reducing false positives in coronary artery disease diagnosis

Innovation

• Multichannel preprocessing combines CLAHE with improved Ben Graham method
• UNet with DenseNet121 encoder and Self-ONN decoder preserves vessel continuity
• Three-stage pipeline includes preprocessing, segmentation, and contour refinement