Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a prom...

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Detalles Bibliográficos
Autores principales: D’Aviero, Andrea, Re, Alessia, Catucci, Francesco, Piccari, Danila, Votta, Claudio, Piro, Domenico, Piras, Antonio, Di Dio, Carmela, Iezzi, Martina, Preziosi, Francesco, Menna, Sebastiano, Quaranta, Flaviovincenzo, Boschetti, Althea, Marras, Marco, Miccichè, Francesco, Gallus, Roberto, Indovina, Luca, Bussu, Francesco, Valentini, Vincenzo, Cusumano, Davide, Mattiucci, Gian Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329735/
https://www.ncbi.nlm.nih.gov/pubmed/35897425
http://dx.doi.org/10.3390/ijerph19159057

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329735/
https://www.ncbi.nlm.nih.gov/pubmed/35897425
http://dx.doi.org/10.3390/ijerph19159057

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