Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiothe...
Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network
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Author / Creator
Liu, Zhikai , Liu, Fangjie , Chen, Wanqi , Tao, Yinjie , Liu, Xia , Zhang, Fuquan , Shen, Jing , Guan, Hui , Zhen, Hongnan , Wang, Shaobin , Chen, Qi , Chen, Yu and Hou, Xiaorong
Publisher
New Zealand: Dove Medical Press Limited
Journal title
Language
English
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Publisher
New Zealand: Dove Medical Press Limited
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Contents
Delineation of clinical target volume (CTV) and organs at risk (OARs) is important for radiotherapy but is time-consuming. We trained and evaluated a U-ResNet model to provide fast and consistent auto-segmentation.
We collected 160 patients' CT scans with breast cancer who underwent breast-conserving surgery (BCS) and were treated with radiother...
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Full title
Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network
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TN_cdi_doaj_primary_oai_doaj_org_article_c780aeb9ac854d3e819552484d5ad4c0
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c780aeb9ac854d3e819552484d5ad4c0
Other Identifiers
ISSN
1179-1322
E-ISSN
1179-1322
DOI
10.2147/CMAR.S330249
