Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning

INTRODUCTION: Organ-at-risk segmentation for head and neck cancer radiation therapy is a complex and time-consuming process (requiring up to 42 individual structure, and may delay start of treatment or even limit access to function-preserving care. Feasibility of using a deep learning (DL) based aut...

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Detalles Bibliográficos
Autores principales: Lucido, J. John, DeWees, Todd A., Leavitt, Todd R., Anand, Aman, Beltran, Chris J., Brooke, Mark D., Buroker, Justine R., Foote, Robert L., Foss, Olivia R., Gleason, Angela M., Hodge, Teresa L., Hughes, Cían O., Hunzeker, Ashley E., Laack, Nadia N., Lenz, Tamra K., Livne, Michelle, Morigami, Megumi, Moseley, Douglas J., Undahl, Lisa M., Patel, Yojan, Tryggestad, Erik J., Walker, Megan Z., Zverovitch, Alexei, Patel, Samir H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115982/
https://www.ncbi.nlm.nih.gov/pubmed/37091160
http://dx.doi.org/10.3389/fonc.2023.1137803

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115982/
https://www.ncbi.nlm.nih.gov/pubmed/37091160
http://dx.doi.org/10.3389/fonc.2023.1137803

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