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Title: Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks

Authors:
 [1];  [2];  [2];  [3];  [3]
  1. 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
  2. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an Shaanxi 710071 China
  3. 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}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1002/mp.13147

Citation Metrics:
Cited by: 7 works
Citation information provided by
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