Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks
- Authors:
-
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an Shaanxi 710071 China, Department of Radiation Oncology, University of California-Los Angeles, Los Angeles CA 90095 USA
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an Shaanxi 710071 China
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles CA 90095 USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1472187
- Grant/Contract Number:
- SC0017057; SC0017687
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Medical Physics
- Additional Journal Information:
- Journal Name: Medical Physics Journal Volume: 45 Journal Issue: 10; Journal ID: ISSN 0094-2405
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Tong, Nuo, Gou, Shuiping, Yang, Shuyuan, Ruan, Dan, and Sheng, Ke. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. United States: N. p., 2018.
Web. doi:10.1002/mp.13147.
Tong, Nuo, Gou, Shuiping, Yang, Shuyuan, Ruan, Dan, & Sheng, Ke. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. United States. doi:10.1002/mp.13147.
Tong, Nuo, Gou, Shuiping, Yang, Shuyuan, Ruan, Dan, and Sheng, Ke. Wed .
"Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks". United States. doi:10.1002/mp.13147.
@article{osti_1472187,
title = {Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks},
author = {Tong, Nuo and Gou, Shuiping and Yang, Shuyuan and Ruan, Dan and Sheng, Ke},
abstractNote = {},
doi = {10.1002/mp.13147},
journal = {Medical Physics},
number = 10,
volume = 45,
place = {United States},
year = {2018},
month = {9}
}
Free Publicly Available Full Text
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DOI: 10.1002/mp.13147
DOI: 10.1002/mp.13147
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Cited by: 10 works
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