Feasibility of automated planning for whole‐brain radiation therapy using deep learning

PURPOSE: The purpose of this study was to develop automated planning for whole‐brain radiation therapy (WBRT) using a U‐net‐based deep‐learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes. METHODS: A dataset of 55 cases, including 40 training sets, fi...

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Detalles Bibliográficos
Autores principales: Yu, Jesang, Goh, Youngmoon, Song, Kye Jin, Kwak, Jungwon, Cho, Byungchul, Kim, Su San, Lee, Sang‐wook, Choi, Eun Kyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Radiation Oncology Physics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856520/
https://www.ncbi.nlm.nih.gov/pubmed/33340391
http://dx.doi.org/10.1002/acm2.13130

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856520/
https://www.ncbi.nlm.nih.gov/pubmed/33340391
http://dx.doi.org/10.1002/acm2.13130

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