Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

Lempart, Michael; Scherman, Jonas; Nilsson, Martin P; Jamtheim Gustafsson, Christian

Lempart, Michael; Scherman, Jonas; Nilsson, Martin P; Jamtheim Gustafsson, Christian.

Afiliação

Lempart M; Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.
Scherman J; Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden.
Nilsson MP; Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.
Jamtheim Gustafsson C; Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.

J Appl Clin Med Phys ; 24(9): e14022, 2023 Sep.

Article em En | MEDLINE | ID: mdl-37177830

Assuntos

Aprendizado Profundo; Neoplasias Pélvicas; Masculino; Humanos; Órgãos em Risco; Neoplasias Pélvicas/radioterapia; Pelve/diagnóstico por imagem; Bexiga Urinária/diagnóstico por imagem; Processamento de Imagem Assistida por Computador/métodos

Palavras-chave

deep learning; guideline; organs at risk; radiation therapy; structure classification

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Pélvicas / Aprendizado Profundo Tipo de estudo: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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