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 |
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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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