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Automated identification and grading of coronary artery stenoses with X-ray angiography
Tao Wan1, Hongxiang Feng2, Chao Tong3
1School of Biomedical Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing 100083, China.
Insights
This study presents an automated method for detecting and grading coronary artery stenosis in X-ray coronary angiography (XCA). The novel approach accurately quantifies stenosis severity, improving cardiovascular disease diagnosis.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Computational Cardiology
Background:
- X-ray coronary angiography (XCA) is the gold standard for diagnosing and treating cardiovascular disease.
- Automatic detection of coronary stenosis in XCA is challenging due to image complexities like overlapping structures and intensity variations.
- Accurate identification and quantification of stenosis severity are crucial for effective patient management.
Purpose of the Study:
- To develop and validate a novel computerized, image-based method for automatic detection and grading of coronary stenoses in XCA.
- To accurately identify and quantify stenosis severity, addressing the limitations of current diagnostic techniques.
- To provide a robust tool for enhancing the analysis of coronary artery disease.
Main Methods:
- A unified framework integrating Hessian-based vessel enhancement, level-set skeletonization, improved measure of match, and local extremum identification was employed.
- The methodology was designed to distinctly reveal vessel structures and accurately determine stenosis grades.
- Validation was performed on 143 consecutive patients undergoing diagnostic XCA, with both qualitative and quantitative assessments.
Main Results:
- The algorithm was tested on 267 vessel segments, achieving high performance metrics.
- Average detection accuracy reached 93.93%, sensitivity 91.03%, specificity 93.83%, and F-score 89.18%.
- The method demonstrated effectiveness in localizing and quantifying vessel stenoses.
Conclusions:
- A fully automatic method for coronary artery stenosis detection and grading in XCA has been developed.
- The presented approach offers a potentially generalized framework applicable to various imaging modalities.
- This automated analysis has the potential to significantly aid in the diagnosis and management of coronary artery disease.
Background And Objective:
X-ray coronary angiography (XCA) remains the gold standard imaging technique for the diagnosis and treatment of cardiovascular disease. Automatic detection and grading of coronary stenoses in XCA are challenging problems due to the complex overlap of different background structures with intensity inhomogeneities. We present a new computerized image based method to accurately identify and quantify the stenosis severity on XCA.
Methods:
A unified framework, consisting of Hessian-based vessel enhancement, level-set skeletonization, improved measure of match measurement, and local extremum identification, is developed to distinctly reveal the vessel structures and accurately determine the stenosis grades. The methodology was validated on 143 consecutive patients who underwent diagnostic XCA through both qualitative and quantitative evaluations.
Results:
The presented algorithm was tested on a set of 267 vessel segments annotated by two expert cardiologists. The experimental results show that the method can effectively localize and quantify the vessel stenoses, achieving average detection accuracy, sensitivity, specificity, and F-score of 93.93%, 91.03%, 93.83%, 89.18%, respectively.
Conclusions:
A fully automatic coronary analysis method is devised for vessel stenosis detection and grading in XCA. The presented approach can potentially serve as a generalized framework to handle different image modalities.
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