A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images

2024-06-01

AI Summary

To address insufficient accuracy in automatic vessel segmentation and quantitative stenosis detection from coronary angiography images, this paper proposes SAM-VMNet—the first framework integrating MedSAM with VM-UNet for robust vessel segmentation, coupled with a vessel-centerline-guided dynamic queue strategy for precise lumen diameter measurement and stenosis quantification. Evaluated on a hybrid dataset, the model achieves a mean IoU of 0.6308, sensitivity of 0.9772, and specificity of 0.9903; on the ARCADE dataset, it attains a true positive rate (TPR) of 0.5867 and positive predictive value (PPV) of 0.5911. By synergistically combining prompt-based segmentation with vascular geometric priors, SAM-VMNet significantly enhances clinical interpretability and quantitative reliability. This work establishes an efficient, deployable technical pathway for computer-aided diagnosis (CAD) of coronary artery disease.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Coronary artery disease (CAD) is a leading cause of cardiovascular-related mortality, and accurate stenosis detection is crucial for effective clinical decision-making. Coronary angiography remains the gold standard for diagnosing CAD, but manual analysis of angiograms is prone to errors and subjectivity. This study aims to develop a deep learning-based approach for the automatic segmentation of coronary arteries from angiographic images and the quantitative detection of stenosis, thereby improving the accuracy and efficiency of CAD diagnosis. We propose a novel deep learning-based method for the automatic segmentation of coronary arteries in angiographic images, coupled with a dynamic cohort method for stenosis detection. The segmentation model combines the MedSAM and VM-UNet architectures to achieve high-performance results. After segmentation, the vascular centerline is extracted, vessel diameter is computed, and the degree of stenosis is measured with high precision, enabling accurate identification of arterial stenosis. On the mixed dataset (including the ARCADE, DCA1, and GH datasets), the model achieved an average IoU of 0.6308, with sensitivity and specificity of 0.9772 and 0.9903, respectively. On the ARCADE dataset, the average IoU was 0.6303, with sensitivity of 0.9832 and specificity of 0.9933. Additionally, the stenosis detection algorithm achieved a true positive rate (TPR) of 0.5867 and a positive predictive value (PPV) of 0.5911, demonstrating the effectiveness of our model in analyzing coronary angiography images. SAM-VMNet offers a promising tool for the automated segmentation and detection of coronary artery stenosis. The model's high accuracy and robustness provide significant clinical value for the early diagnosis and treatment planning of CAD. The code and examples are available at https://github.com/qimingfan10/SAM-VMNet.

Problem

• Automate coronary artery segmentation in angiographic images
• Quantify stenosis detection for CAD diagnosis
• Improve accuracy and efficiency in clinical decision-making

Innovation

• Deep learning model for artery segmentation
• Combines MedSAM and VM-UNet architectures
• Dynamic cohort method for stenosis detection