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Proceedings Paper

Automated coronary artery segmentation in Coronary Computed Tomography Angiography (CCTA) using deep learning neural networks
Author(s): Yang Lei; Bangjun Guo; Yabo Fu; Tonghe Wang; Tian Liu; Walter Curran; Longjiang Zhang; Xiaofeng Yang
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Paper Abstract

Automatic segmentation of the coronary artery in coronary computed tomographic angiography (CCTA) is important for clinicians in evaluating patients with coronary artery disease (CAD). Tradition visual interpretation of coronary artery stenosis is observer-dependent and time-consuming. In this work, we proposed to use a 3D attention fully convolution network (FCN) method to automatically segment the coronary artery for CCTA. FCN was used to perform end-to-end mapping from CCTA image to the binary segmentation of coronary artery. Deep attention strategy was integrated into the FCN model to highlight the informative semantic features extracted from CCTA image and thus to enhance the accuracy of segmentation. The proposed method was tested on 30 patients’ CCTA data. Dice similarity coefficient (DSC), precision and recall indices between manually delineated coronary artery contour and segmented contour were used to quantify the segmentation accuracy of the proposed method. The DSC, precision, and recall were 83%±4%, 84%±4% and 87%±3%, which demonstrated the segmentation accuracy of the proposed method.

Paper Details

Date Published: 2 March 2020
PDF: 6 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131812 (2 March 2020); doi: 10.1117/12.2550368
Show Author Affiliations
Yang Lei, The Winship Cancer Institute of Emory Univ. (United States)
Bangjun Guo, The Winship Cancer Institute of Emory Univ. (United States)
Southern Medical Univ. (China)
Medical School of Nanjing Univ. (China)
Yabo Fu, The Winship Cancer Institute of Emory Univ. (United States)
Tonghe Wang, The Winship Cancer Institute of Emory Univ. (United States)
Tian Liu, The Winship Cancer Institute of Emory Univ. (United States)
Walter Curran, The Winship Cancer Institute of Emory Univ. (United States)
Longjiang Zhang, Southern Medical Univ. (China)
Medical School of Nanjing Univ. (China)
Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

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