Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks-Reference-Cited by-同舟云学术

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

Published:2018-09-19 Issue:10 Volume:45 Page:4558-4567
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Tong Nuo¹²,Gou Shuiping¹,Yang Shuyuan¹,Ruan Dan²,Sheng Ke²

Affiliation:

1. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an Shaanxi 710071 China

2. Department of Radiation Oncology University of California—Los Angeles Los Angeles CA 90095 USA

Funder

U.S. Department of Energy

National Institutes of Health

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13147

Reference37 articles.

1. Worldwide Trends in Incidence Rates for Oral Cavity and Oropharyngeal Cancers

2. IMRT for head and neck cancer: reducing xerostomia and dysphagia

3. A System for Continual Quality Improvement of Normal Tissue Delineation for Radiation Therapy Treatment Planning

4. Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015

5. HanX HoogemanMS LevendagPC et al.Atlas‐based auto‐segmentation of head and neck CT images. In:MICCAI Proceedings;2008:434–441.

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