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European Radiology
Published in: European Radiology 9/2022

08-04-2022 | CT Angiography | Cardiac

Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography

Authors: Yiming Li, Yu Wu, Jingjing He, Weili Jiang, Jianyong Wang, Yong Peng, Yuheng Jia, Tianyuan Xiong, Kaiyu Jia, Zhang Yi, Mao Chen

Published in: European Radiology | Issue 9/2022

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Abstract

Objectives

Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort.

Methods

Automatic CAD diagnosis consists of two subtasks. One is a segmentation task, which aims to extract the region of interest (ROI) from original images with U-Net. The second task is an identification task, which we implemented using 3DNet. The coronary artery tree images and clinical parameters were input into 3DNet, and the CAD diagnosis result was output.

Results

We built a coronary artery segmentation model based on CCTA images with the corresponding labelling. The segmentation model had a mean Dice value of 0.771 ± 0.021. Based on this model, we built an automated diagnosis model (classification model) for CAD. The average accuracy and area under the receiver operating characteristic curve (AUC) were 0.750 ± 0.056 and 0.737, respectively.

Conclusion

Herein, using a deep learning algorithm, we realized the rapid classification and diagnosis of CAD from CCTA images in two steps. Our deep learning model can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis. Artificial intelligence methods such as deep learning have the potential to elevate the efficiency in CCTA image analysis considerably.

Key Points

• The deep learning model rapidly achieved a high Dice value (0.771 ± 0.0210) in the autosegmentation of coronary arteries using CCTA images.
• Based on the segmentation model, we built a CAD autoclassifier with the 3DNet algorithm, which achieved a good diagnostic performance (AUC) of 0.737.
• The deep neural network could be used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.
Appendix
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Metadata
Title
Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography
Authors
Yiming Li
Yu Wu
Jingjing He
Weili Jiang
Jianyong Wang
Yong Peng
Yuheng Jia
Tianyuan Xiong
Kaiyu Jia
Zhang Yi
Mao Chen
Publication date
08-04-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 9/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08761-z

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