Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.

Journal: Radiation oncology (London, England)

Published Date: Jun 8, 2021

Abstract

PURPOSE: We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical radiotherapy (RT) planning workflow and report on user experience.

Authors

Jordan Wong

BC Cancer - Vancouver Center, Canada. Electronic address: Jordan.wong@bccancer.bc.ca.
Vicky Huang

BC Cancer - Fraser Valley, 13750 96th Avenue, Surrey, BC, V3V 1Z2, Canada.
Derek Wells

BC Cancer - Victoria Center, Canada. Electronic address: DWells@bccancer.bc.ca.
Joshua Giambattista

Saskatchewan Cancer Agency, Regina, Canada; Limbus AI Inc., Regina, Canada. Electronic address: joshua.giambattista@saskcancer.ca.
Jonathan Giambattista

Limbus AI Inc., Regina, Canada. Electronic address: jon@limbus.ai.
Carter Kolbeck
Karl Otto

Limbus AI Inc, 2076 Athol Street, Regina, SK, S4T 3E5, Canada.
Elantholi P Saibishkumar

BC Cancer - Victoria, 2410 Lee Avenue, Victoria, BC, V8R 6V5, Canada.
Abraham Alexander

BC Cancer - Victoria Center, Canada. Electronic address: AAlexander3@bccancer.bc.ca.

Keywords

Algorithms Central Nervous System Neoplasms Deep Learning Head and Neck Neoplasms Health Plan Implementation Humans Image Processing, Computer-Assisted Male Organs at Risk Prognosis Prostatic Neoplasms Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed Workflow

External Resources

View on PubMed Access via DOI PubMed (34103062)

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.

Abstract

Authors

Keywords

External Resources

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