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A Semi-Automatic Coronary Artery Segmentation Framework Using Mechanical Simulation
Ken Cai1, Rongqian Yang, Lihua Li
1School of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Zhongkai Road 501#, Guangzhou, 510225, Guangdong, China.
Insights
This study introduces a novel method for precisely measuring coronary artery inner diameter using computed tomography images. This technique aids in the early and quantitative diagnosis of cardiovascular disease (CVD) and coronary artery stenosis.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Diagnosis
- Biomedical Engineering
Background:
- Cardiovascular disease (CVD) poses a significant global health threat.
- Early and quantitative diagnosis of CVD is crucial for improving patient outcomes and longevity.
- Coronary artery stenosis diagnosis requires accurate measurement of the coronary artery's inner diameter.
Purpose of the Study:
- To develop and validate a method for precise centerline extraction of coronary arteries from computed tomography (CT) images.
- To accurately measure the inner diameter of coronary arteries for diagnosing stenosis.
- To provide a foundation for quantitative diagnosis of coronary artery stenosis.
Main Methods:
- Coronary artery segmentation using morphology and inter-slice continuity from CT images.
- Centerline extraction via mechanical simulation of image pixels as mass points with tensile forces.
- Local line-fitting for outlining the coronary artery centerline.
- Nearest point method for inner diameter measurement.
Main Results:
- The proposed method precisely extracts the coronary artery centerline.
- Accurate measurement of the coronary artery's inner diameter was achieved.
- The technique provides a reliable basis for quantitative diagnosis of coronary artery stenosis.
Conclusions:
- The developed method offers accurate centerline extraction and inner diameter measurement for coronary arteries.
- This approach supports the early and quantitative diagnosis of cardiovascular disease, specifically coronary artery stenosis.
- The findings contribute to improved diagnostic capabilities for cardiovascular conditions.
Abstract:
CVD (cardiovascular disease) is one of the biggest threats to human beings nowadays. An early and quantitative diagnosis of CVD is important in extending lifespan and improving people's life quality. Coronary artery stenosis can prevent CVD. To diagnose the degree of stenosis, the inner diameter of coronary artery needs to be measured. To achieve such measurement, the coronary artery is segmented by using a method that is based on morphology and the continuity between computed tomography image slices. A centerline extraction method based on mechanical simulation is proposed. This centerline extraction method can figure out a basic framework of the coronary artery by simulating pixel dots of the artery image into mass points. Such mass points have tensile forces, with which the outer pixel dots can be drawn to the center. Subsequently, the centerline of the coronary artery can be outlined by using the local line-fitting method. Finally, the nearest point method is adopted to measure the inner diameter. Experimental results showed that the methods proposed in this paper can precisely extract the centerline of the coronary artery and can accurately measure its inner diameter, thereby providing a basis for quantitative diagnosis of coronary artery stenosis.
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