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Automated coronary artery tree segmentation in coronary CTA using a multiobjective clustering and toroidal
Hongwei Du1, Kai Shao2, Fangxun Bao3
1School of Mathmatics, Shandong University, Jinan, Shandong 250100, China; Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, Shandong 250014, China.
Insights
This study introduces a new method for segmenting coronary arteries in computed tomography angiography (CTA) images, improving accuracy for detecting coronary artery disease.
Area of Science:
- Medical Imaging
- Cardiovascular Research
- Image Analysis
Background:
- Coronary artery segmentation is crucial for diagnosing coronary artery disease.
- Challenges include image noise, low contrast, and complex vessel structures.
- Accurate segmentation aids radiologists in clinical detection.
Purpose of the Study:
- To develop an accurate method for segmenting 3D coronary artery trees from CTA images.
- To overcome segmentation challenges posed by medical image quality and anatomical complexity.
Main Methods:
- A novel framework combining multiobjective clustering and toroidal model-guided tracking.
- Integrated noise reduction, candidate region detection, and geometric feature extraction.
- Candidate regions identified via multiobjective clustering; arteries tracked using toroidal model guidance.
Main Results:
- The proposed framework demonstrates superior performance compared to existing methods.
- Achieved a mean Dice Similarity Coefficient (DSC) of 84%, Jaccard index of 74%, and Recall of 93%.
- Qualitative and quantitative results validate the method's effectiveness on CTA data.
Conclusions:
- The developed segmentation framework accurately segments coronary artery trees from CTA volumes.
- This advancement enhances the precision of 3D vascular tree segmentation for clinical applications.
Background And Objective:
Accurate coronary artery tree segmentation can now be developed to assist radiologists in detecting coronary artery disease. In clinical medicine, the noise, low contrast, and uneven intensity of medical images along with complex shapes and vessel bifurcation structures make coronary artery segmentation challenging. In this work, we propose a multiobjective clustering and toroidal model-guided tracking method that can accurately extract coronary arteries from computed tomography angiography (CTA) imagery.
Methods:
Utilizing integrated noise reduction, candidate region detection, geometric feature extraction, and coronary artery tracking techniques, a new segmentation framework for 3D coronary artery trees is presented. The candidate regions are extracted using a multiobjective clustering method, and the coronary arteries are tracked by a toroidal model-guided tracking method.
Results:
The qualitative and quantitative results demonstrate the effectiveness of the presented framework, which achieves better performance than the compared segmentation methods in three widely used evaluation indices: the Dice similarity coefficient (DSC), Jaccard index and Recall across the CTA data. The proposed method can accurately identify the coronary artery tree with a mean DSC of 84%, a Jaccard index of 74%, and a Recall of 93%.
Conclusions:
The proposed segmentation framework effectively segments the coronary tree from the CTA volume, which improves the accuracy of 3D vascular tree segmentation.
