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A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy.

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第一作者： Monjoy,Saha

第一单位： Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.

作者： Monjoy,Saha ^[1] ; Jae Won,Jung ^[2] ; Sung-Woo,Lee ^[3] ; Choonik,Lee ^[4] ; Choonsik,Lee ^[1] ; Matthew M,Mille ^[1]

作者单位： Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA. ^[1] Department of Radiation Oncology, East Carolina University, Greenville, NC 27834, USA. ^[2] Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21044, USA. ^[3] Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA. ^[4]

关键词 Artificial intelligence Breast radiotherapy Computed tomography Deep learning Image segmentation Late effects

DOI 10.1016/j.phro.2023.100520

PMID 38077272

发布时间 2023-12-11

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Physics and imaging in radiation oncology

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A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy.

Physics and imaging in radiation oncology

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A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy.

Physics and imaging in radiation oncology

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Physics and imaging in radiation oncology

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