Continuous extraction of coronary artery centerline from cardiac CTA images using a regression-based method

Xintong Wu; Yingyi Geng; Xinhong Wang; Jucheng Zhang; Ling Xia; Xintong Wu; Yingyi Geng; Xinhong Wang; Jucheng Zhang; Ling Xia

doi:10.3934/mbe.2023231

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 4988-5003. doi: 10.3934/mbe.2023231

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Continuous extraction of coronary artery centerline from cardiac CTA images using a regression-based method

1.
Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
2.
Department of Radiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
3.
Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China

† These authors contributed equally to this study.
Academic Editor: Hao Wang

Received: 24 November 2022 Revised: 23 December 2022 Accepted: 26 December 2022 Published: 06 January 2023

Coronary artery centerline extraction in cardiac computed tomography angiography (CTA) is an effectively non-invasive method to diagnose and evaluate coronary artery disease (CAD). The traditional method of manual centerline extraction is time-consuming and tedious. In this study, we propose a deep learning algorithm that continuously extracts coronary artery centerlines from CTA images using a regression method. In the proposed method, a CNN module is trained to extract the features of CTA images, and then the branch classifier and direction predictor are designed to predict the most possible direction and lumen radius at the given centerline point. Besides, a new loss function is developed for associating the direction vector with the lumen radius. The whole process starts from a point manually placed at the coronary artery ostia, and terminates until tracking the vessel endpoint. The network was trained using a training set consisting of 12 CTA images and the evaluation was performed using a testing set consisting of 6 CTA images. The extracted centerlines had an average overlap (OV) of 89.19%, overlap until first error (OF) of 82.30%, and overlap with clinically relevant vessel (OT) of 91.42% with manually annotated reference. Our proposed method can efficiently deal with multi-branch problems and accurately detect distal coronary arteries, thereby providing potential help in assisting CAD diagnosis.
- coronary artery,
- CTA,
- centerline extraction,
- deep learning
Citation: Xintong Wu, Yingyi Geng, Xinhong Wang, Jucheng Zhang, Ling Xia. Continuous extraction of coronary artery centerline from cardiac CTA images using a regression-based method[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4988-5003. doi: 10.3934/mbe.2023231

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Abstract

Coronary artery centerline extraction in cardiac computed tomography angiography (CTA) is an effectively non-invasive method to diagnose and evaluate coronary artery disease (CAD). The traditional method of manual centerline extraction is time-consuming and tedious. In this study, we propose a deep learning algorithm that continuously extracts coronary artery centerlines from CTA images using a regression method. In the proposed method, a CNN module is trained to extract the features of CTA images, and then the branch classifier and direction predictor are designed to predict the most possible direction and lumen radius at the given centerline point. Besides, a new loss function is developed for associating the direction vector with the lumen radius. The whole process starts from a point manually placed at the coronary artery ostia, and terminates until tracking the vessel endpoint. The network was trained using a training set consisting of 12 CTA images and the evaluation was performed using a testing set consisting of 6 CTA images. The extracted centerlines had an average overlap (OV) of 89.19%, overlap until first error (OF) of 82.30%, and overlap with clinically relevant vessel (OT) of 91.42% with manually annotated reference. Our proposed method can efficiently deal with multi-branch problems and accurately detect distal coronary arteries, thereby providing potential help in assisting CAD diagnosis.

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