Background In order to accurately accumulate delivered dose for head and neck cancer
patients treated with the Adapt to Position workflow on the 1.5 T magnetic resonance …

Background MR-Linac allows for daily online treatment adaptation to the observed geometry
of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer …

Purpose The use of magnetic resonance imaging (MRI) in radiotherapy treatment planning
has rapidly increased due to its ability to evaluate patient's anatomy without the use of …

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 …

Atlas‐based autosegmentation is an established tool for segmenting structures for CT‐
planned head and neck radiotherapy. MRI is being increasingly integrated into the planning …

Results Similarity metrics did not or moderately correlate with the accuracy of the auto-
segmentation (median R2 of 0.2, range, 0.0-0.7). The mean Dice coefficient/STSD (mm) of …

Background Auto‐segmentation of organs‐at‐risk (OARs) in the head and neck (HN) on
computed tomography (CT) images is a time‐consuming component of the radiation therapy …

Background and purpose Deformable image registration (DIR) and contour propagation are
used in daily online adaptation for hybrid MRI linac (MRL) treatments. The accuracy of the …

Purpose Adaptive radiotherapy requires auto‐segmentation in patients with head and neck
(HN) cancer. In the current study, we propose an auto‐segmentation model using a …

PURPOSE: To validate and clinically evaluate autocontouring using atlas-based
autosegmentation (ABAS) of computed tomography images. METHODS AND MATERIALS …