Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network

BACKGROUND: Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automa...

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Detalles Bibliográficos
Autores principales: Lempart, Michael, Nilsson, Martin P., Scherman, Jonas, Gustafsson, Christian Jamtheim, Nilsson, Mikael, Alkner, Sara, Engleson, Jens, Adrian, Gabriel, Munck af Rosenschöld, Per, Olsson, Lars E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238000/
https://www.ncbi.nlm.nih.gov/pubmed/35765038
http://dx.doi.org/10.1186/s13014-022-02088-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238000/
https://www.ncbi.nlm.nih.gov/pubmed/35765038
http://dx.doi.org/10.1186/s13014-022-02088-1

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