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

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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Bibliographic Details
Main Authors: E. Tryggestad, A. Anand, C. Beltran, J. Brooks, J. Cimmiyotti, N. Grimaldi, T. Hodge, A. Hunzeker, J. J. Lucido, N. N. Laack, R. Momoh, D. J. Moseley, S. H. Patel, A. Ridgway, S. Seetamsetty, S. Shiraishi, L. Undahl, R. L. Foote
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-08-01
Series:Frontiers in Oncology
Subjects:
curation
artificial intelligence
deep learning
convolutional neural network
autosegmentation
radiotherapy
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.936134/full

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https://www.frontiersin.org/articles/10.3389/fonc.2022.936134/full

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