Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network

Fan Fu, Jianyong Wei, Miao Zhang, Fan Yu, Yueting Xiao, Dongdong Rong, Yi Shan, Yan Li, Cheng Zhao, Fangzhou Liao, Zhenghan Yang, Yuehua Li, Yingmin Chen, Ximing Wang and Jie Lu ()
Additional contact information
Fan Fu: Capital Medical University
Jianyong Wei: Shukun (Beijing) Technology Co., Ltd.
Miao Zhang: Capital Medical University
Fan Yu: Capital Medical University
Yueting Xiao: Shukun (Beijing) Technology Co., Ltd.
Dongdong Rong: Capital Medical University
Yi Shan: Capital Medical University
Yan Li: Capital Medical University
Cheng Zhao: Capital Medical University
Fangzhou Liao: Chinese Academy of Sciences
Zhenghan Yang: Friendship Hospital, Capital Medical University
Yuehua Li: Shanghai Jiao Tong University Affiliated Sixth People’s Hospital
Yingmin Chen: Hebei General Hospital
Ximing Wang: Shandong Provincial Hospital
Jie Lu: Capital Medical University

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.

Date: 2020
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DOI: 10.1038/s41467-020-18606-2

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