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

doi:10.1002/mp.13147

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

Authors:

Tong, Nuo ^[1]; Gou, Shuiping ^[2]; Yang, Shuyuan ^[2]; Ruan, Dan ^[3]; Sheng, Ke ^[3]

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:: 2018-09-19

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.

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                    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.

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                    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.

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@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}

}

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DOI: 10.1002/mp.13147

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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

Citation Formats

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Active Shape Models-Their Training and Application journal, January 1995

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Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks
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Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015
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Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
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journal, May 2016

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation
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Worldwide Trends in Incidence Rates for Oral Cavity and Oropharyngeal Cancers
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Randomized Phase III Trial of Concurrent Accelerated Radiation Plus Cisplatin With or Without Cetuximab for Stage III to IV Head and Neck Carcinoma: RTOG 0522
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