Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy

Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy

Background and Purpose: Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased. Materials and Meth...

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Bibliographic Details
Main Authors: Maria Thor, Aditi Iyer, Jue Jiang, Aditya Apte, Harini Veeraraghavan, Natasha B. Allgood, Jennifer A. Kouri, Ying Zhou, Eve LoCastro, Sharif Elguindi, Linda Hong, Margie Hunt, Laura Cerviño, Michalis Aristophanous, Masoud Zarepisheh, Joseph O. Deasy
Format: Article
Language:English
Published: Elsevier 2021-07-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Cancer
Radiation
Head neck
Deep learning
Masseter
Medial pterygoid
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631621000440

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http://www.sciencedirect.com/science/article/pii/S2405631621000440

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