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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...
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. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
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 |
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