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

Autor: Yingmin Chen, Cheng Zhao, Yan Li, Ximing Wang, Dongdong Rong, Fan Fu, Yueting Xiao, Fangzhou Liao, Fan Yu, Jianyong Wei, Yang Zhenghan, Yi Shan, Miao Zhang, Yuehua Li, Jie Lu
Rok vydání: 2020
Předmět:
Male
Mathematics and computing
Computer science
General Physics and Astronomy
Vessel segmentation
Convolutional neural network
030218 nuclear medicine & medical imaging
0302 clinical medicine
Image Processing
Computer-Assisted
Computer vision
lcsh:Science
Head and neck
Computed tomography angiography
Multidisciplinary
medicine.diagnostic_test
Angiography
Middle Aged
Neurology
Female
Tomography
China
Science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Article
Bone and Bones
General Biochemistry
Genetics and Molecular Biology
Patient care
03 medical and health sciences
Medical research
Imaging
Three-Dimensional
Machine learning
medicine
Humans
Aged
ComputingMethodologies_COMPUTERGRAPHICS
business.industry
General Chemistry
Workflow
Blood Vessels
lcsh:Q
Artificial intelligence
Nerve Net
Tomography
X-Ray Computed
business
Head
Neck
030217 neurology & neurosurgery
Zdroj: Nature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
Nature Communications
ISSN: 2041-1723
Popis: 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.
Manual postprocessing of computed tomography angiography (CTA) images is extremely labor intensive and error prone. Here, the authors propose an artificial intelligence reconstruction system that can automatically achieve CTA reconstruction in healthcare services.
Databáze: OpenAIRE
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