A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer.

Chen, Along; Chen, Fei; Li, Xiaofang; Zhang, Yazhi; Chen, Li; Chen, Lixin; Zhu, Jinhan

Chen, Along; Chen, Fei; Li, Xiaofang; Zhang, Yazhi; Chen, Li; Chen, Lixin; Zhu, Jinhan.

Afiliação

Chen A; Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Chen F; School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China.
Li X; Department of Radiation Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Zhang Y; Department of Oncology and Hematology, The Six People's Hospital of Huizhou City, Huiyang Hospital Affiliated to Southern Medical University, Huizhou, China.
Chen L; Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Chen L; Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Zhu J; Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.

Front Oncol ; 12: 908903, 2022.

Article em En | MEDLINE | ID: mdl-35719942

Palavras-chave

automatic segmentation; cervical cancer; deep learning; dosimetric differences; geometric accuracy

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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