Journal article

Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging

James C Korte, Nicholas Hardcastle, Sweet Ping Ng, Brett Clark, Tomas Kron, Price Jackson

MEDICAL PHYSICS | WILEY | Published : 2021


PURPOSE: To investigate multiple deep learning methods for automated segmentation (auto-segmentation) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI). Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time-consuming task, in which variability between observers may directly impact on patient treatment outcomes. Auto-segmentation on computed tomography imaging has been shown to result in significant time reductions and more consistent outlines of the organs at risk. METHODS: Three convolutional neural network (CNN)-based auto-segmentat..

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Awarded by Peter MacCallum Cancer Foundation

Funding Acknowledgements

This project was supported by funding from the Peter MacCallum Cancer Foundation. This research was undertaken using the LIEF HPC-GPGPU Facility established with the assistance of LIEF Grant LE170100200 and hosted at the University of Melbourne.