Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy.

ABSTRACT:

Background and purpose

Treatments on combined Magnetic Resonance (MR) scanners and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily contouring. Currently, deformable image registration (DIR) algorithms propagate contours from reference scans, however large shape and size changes can be troublesome. Artificial neural network (ANN) based contouring may alleviate this issue, however generally requires large datasets for training. Mitigating the problem of scarcity of data, we propose patient specific networks trained on a single dataset for each patient, for contouring onto the following datasets in an adaptive MR-Linacworkflow.

Materials and methods

MR-scans from 17 prostate patients treated on an MR-Linac with contours of Clinical Target Vo

SUBMITTER: Fransson S 

PROVIDER: S-EPMC9234226 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature