S. Elguindi, M. Zelefsky, Jue Jiang, H. Veeraraghavan, J. Deasy, M. Hunt, N. Tyagi
tlooto Summary
A deep learning-based model produced contours that show promise to streamline an MR-only planning workflow in treating prostate cancer and significantly outperformed U-Net for all structures except urethra.
Abstract
Highlights • Auto-segmentation using Deep Learning can be difficult with a small medical dataset.• Transfer learning allows deep learning networks to retrain easily on small datasets.• We successfully apply this method to auto-segment targets and OARs in prostate radiation therapy.
Citation format
ELGUINDI, S., et al. Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy. Physics & Imaging in Radiation Oncology, 2019, 12: 80–86.