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

Jordan Wong¹, Vicky Huang², Derek Wells³, Joshua Giambattista^4,5, Jonathan Giambattista⁵, Carter Kolbeck⁵, Karl Otto⁵, Elantholi P Saibishkumar³, Abraham Alexander³

¹BC Cancer - Vancouver, 600 W 10th Ave, Rm 4550, Vancouver, BC, V5Z 4E6, Canada. Jordan.wong@bccancer.bc.ca.

²BC Cancer - Fraser Valley, 13750 96th Avenue, Surrey, BC, V3V 1Z2, Canada.

³BC Cancer - Victoria, 2410 Lee Avenue, Victoria, BC, V8R 6V5, Canada.

⁴Saskatchewan Cancer Agency, 503-1801 Hamilton St, Regina, SK, S4P 4B4, Canada.

⁵Limbus AI Inc, 2076 Athol Street, Regina, SK, S4T 3E5, Canada.

PubMed PMC DOI PDF

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. METHODS AND MATERIALS: DC models were implemented at two cancer centers and used to…

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