Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

Zhou, Dejun; Nakamura, Mitsuhiro; Mukumoto, Nobutaka; Matsuo, Yukinori; Mizowaki, Takashi

Zhou, Dejun; Nakamura, Mitsuhiro; Mukumoto, Nobutaka; Matsuo, Yukinori; Mizowaki, Takashi.

Afiliação

Zhou D; Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Nakamura M; Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Mukumoto N; Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Matsuo Y; Department of Radiation Oncology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
Mizowaki T; Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

J Appl Clin Med Phys ; 24(4): e13894, 2023 Apr.

Article em En | MEDLINE | ID: mdl-36576920

Assuntos

Aprendizado Profundo; Neoplasias Pulmonares; Humanos; Estudos de Viabilidade; Estudos Retrospectivos; Raios X; Neoplasias Pulmonares/diagnóstico por imagem; Neoplasias Pulmonares/radioterapia

Palavras-chave

deep learning; markerless real-time tumor tracking; respiratory motion management; target contour prediction

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Appl Clin Med Phys Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google