Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer

In this era of patient-centered, outcomes-driven and adaptive radiotherapy, deep learning is now being successfully applied to tackle imaging-related workflow bottlenecks such as autosegmentation and dose planning. These applications typically require supervised learning approaches enabled by relati...

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Detalles Bibliográficos
Autores principales: Tryggestad, E., Anand, A., Beltran, C., Brooks, J., Cimmiyotti, J., Grimaldi, N., Hodge, T., Hunzeker, A., Lucido, J. J., Laack, N. N., Momoh, R., Moseley, D. J., Patel, S. H., Ridgway, A., Seetamsetty, S., Shiraishi, S., Undahl, L., Foote, R. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464982/
https://www.ncbi.nlm.nih.gov/pubmed/36106100
http://dx.doi.org/10.3389/fonc.2022.936134

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464982/
https://www.ncbi.nlm.nih.gov/pubmed/36106100
http://dx.doi.org/10.3389/fonc.2022.936134

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