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Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network(Open Access)

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收录情况： ◇ SCIE ◇ 自然指数

作者：
Fan Fu [1,2] * ; Jianyong Wei[3] ; Miao Zhang[1,2] ; Fan Yu[1,2] ; Yueting Xiao[3] ; Dongdong Rong[1,2] ; Yi Shan [1,2] ; Yan Li[1,2] ; Cheng Zhao[1,2] ; Fangzhou Liao[4] ; Zhenghan Yang[5] ; Yuehua Li[6] ; Yingmin Chen[7] ; Ximing Wang[8] ; Jie Lu [1,2,9] ;
机构： [1]Department of Radiology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, 100053 Beijing, China. [2]Beijing KeyLaboratory of Magnetic Resonance Imaging and Brain Informatics, No. 45 Changchun Street, Xicheng District, 100053 Beijing, China. [3]Shukun (Beijing)Technology Co., Ltd., Jinhui Building, Qiyang Road, 100102 Beijing, China. [4]Institute of Information Engineering, Chinese Academy of Sciences, No. 52Minzhuang Road, 100093 Beijing, China. [5]Department of Radiology, Friendship Hospital, Capital Medical University, No. 95 Yongan Road, DongchengDistrict, 100050 Beijing, China. [6]Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, No.600, Yi Shan Road, 200233 Shanghai, China. [7]Medical Imaging Department, Hebei General Hospital, No. 348 Hepingxi Street, 050051 Shijiazhuang, Hebei,China. [8]Department of Radiology, Shandong Provincial Hospital, No. 324 Jingwei Road, 250021 Jinan, Shandong, China. [9]Department of Nuclear Medicine,Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, 100053 Beijing, China
出处：
DOI： 10.1038/s41467-020-18606-2
ISSN：

摘要：
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. © 2020, The Author(s).

基金：
This work has been supported by “Beijing Natural Science Foundation (Z190014)” and “Beijing Municipal Administration of Hospital’s Ascent Plan” (DFL20180802) and “Beijing Municipal science and technology projects” (Z201100005620009).
语种：
外文
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中科院(CAS)分区：
出版当年[2019]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
最新[2023]版：
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
JCR分区：
出版当年[2018]版：
Q1 MULTIDISCIPLINARY SCIENCES
最新[2023]版：
Q1 MULTIDISCIPLINARY SCIENCES

影响因子： 最新[2023版] 最新五年平均 出版当年[2018版] 出版当年五年平均 出版前一年[2017版] 出版后一年[2019版]

第一作者：
第一作者机构： [1]Department of Radiology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, 100053 Beijing, China. [2]Beijing KeyLaboratory of Magnetic Resonance Imaging and Brain Informatics, No. 45 Changchun Street, Xicheng District, 100053 Beijing, China.
通讯作者：
推荐引用方式(GB/T 7714)：
Fan Fu,Jianyong Wei,Miao Zhang,et al.Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network(Open Access)[J].NATURE COMMUNICATIONS.2020,11(1):doi:10.1038/s41467-020-18606-2.
APA：
Fan Fu,Jianyong Wei,Miao Zhang,Fan Yu,Yueting Xiao...&Jie Lu.(2020).Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network(Open Access).NATURE COMMUNICATIONS,11,(1)
MLA：
Fan Fu,et al."Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network(Open Access)".NATURE COMMUNICATIONS 11..1(2020)

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