Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma
Abstract 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 posi...
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| Principais autores: | , , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BMC
2022-10-01
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| coleção: | Radiation Oncology |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1186/s13014-022-02137-9 |
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