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Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and
Omar Ibrahim Alirr1, Hamada R H Al-Absi2, Abduladhim Ashtaiwi1
1College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.
Insights
This study presents an improved method for segmenting coronary arteries in CT angiography (CTA) images, enhancing cardiovascular disease diagnosis. The approach achieves high accuracy, aiding in better patient treatment strategies.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Diagnosis
- Artificial Intelligence in Healthcare
Background:
- Accurate segmentation of coronary arteries from CT angiography (CTA) images is essential for diagnosing and treating cardiovascular diseases.
- Current segmentation methods face challenges in accuracy and efficiency.
Purpose of the Study:
- To develop a structured approach for accurate and efficient coronary artery segmentation from CTA images.
- To improve the diagnostic and therapeutic capabilities for cardiovascular diseases.
Main Methods:
- A combination of vesselness enhancement and heart region of interest (ROI) extraction.
- Utilizing the ResUNet deep learning model for feature extraction and segmentation.
- Evaluating performance using metrics like Dice Similarity Coefficient (DSC), Recall, and Precision.
Main Results:
- The proposed method achieved a Dice Similarity Coefficient (DSC) of 0.867.
- Recall reached 0.881, and Precision was 0.892.
- Outperformed existing state-of-the-art segmentation methods.
Conclusions:
- The structured approach significantly enhances the accuracy and efficiency of coronary artery segmentation.
- The method shows strong potential for improving cardiovascular disease diagnosis and treatment planning.
- ResUNet's ability to capture diverse features is key to the method's success.
Abstract:
Accurate and efficient segmentation of coronary arteries from CTA images is crucial for diagnosing and treating cardiovascular diseases. This study proposes a structured approach that combines vesselness enhancement, heart region of interest (ROI) extraction, and the ResUNet deep learning method to accurately and efficiently extract coronary artery vessels. Vesselness enhancement and heart ROI extraction significantly improve the accuracy and efficiency of the segmentation process, while ResUNet enables the model to capture both local and global features. The proposed method outperformed other state-of-the-art methods, achieving a Dice similarity coefficient (DSC) of 0.867, a Recall of 0.881, and a Precision of 0.892. The exceptional results for segmenting coronary arteries from CTA images demonstrate the potential of this method to significantly contribute to accurate diagnosis and effective treatment of cardiovascular diseases.
