Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma

AIMS: To save time and have more consistent contours, fully automatic segmentation of targets and organs at risk (OAR) is a valuable asset in radiotherapy. Though current deep learning (DL) based models are on par with manual contouring, they are not perfect and typical errors, as false positives, o...

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Detalles Bibliográficos
Autores principales: Poel, Robert, Rüfenacht, Elias, Ermis, Ekin, Müller, Michael, Fix, Michael K., Aebersold, Daniel M., Manser, Peter, Reyes, Mauricio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587574/
https://www.ncbi.nlm.nih.gov/pubmed/36273161
http://dx.doi.org/10.1186/s13014-022-02137-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587574/
https://www.ncbi.nlm.nih.gov/pubmed/36273161
http://dx.doi.org/10.1186/s13014-022-02137-9

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