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 …

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 …

Purpose: To develop an automatic segmentation algorithm integrating imaging information
from computed tomography (CT), positron emission tomography (PET), and magnetic …

Purpose Because the manual contouring process is labor‐intensive and time‐consuming,
segmentation of organs‐at‐risk (OARs) is a weak link in radiotherapy treatment planning …

Purpose Online adaptive radiotherapy would greatly benefit from the development of
reliable auto‐segmentation algorithms for organs‐at‐risk and radiation targets. Current …

Purpose: Intensity modulated radiation therapy (IMRT) allows greater control over dose
distribution, which leads to a decrease in radiation related toxicity. IMRT, however, requires …

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 …

Accurate segmentation of organs-at-risk (OAR) is essential for treatment planning of head
and neck (HaN) cancers. A desire to shift from manual segmentation to automated …

Background Accurate segmentation of head and neck squamous cell cancer (HNSCC) is
important for radiotherapy treatment planning. Manual segmentation of these tumors is time …