Artificial Intelligence (AI) is currently being introduced into different domains, including
medicine. Specifically in radiation oncology, machine learning models allow automation and …

Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …

Background: Over half a million individuals are diagnosed with head and neck cancer each
year globally. Radiotherapy is an important curative treatment for this disease, but it requires …

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck
(H&N), which requires a precise spatial description of the target volumes and organs at risk …

Segmentation of organs and structures, as either targets or organs‐at‐risk, has a significant
influence on the success of radiation therapy. Manual segmentation is a tedious and time …

Purpose/objective Precise delineation of organs at risk (OARs) in head and neck cancer
(HNC) is necessary for accurate radiotherapy. Although guidelines exist, significant …

Background In radiotherapy inaccuracy in organ at risk (OAR) delineation can impact
treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant …

Medical imaging techniques, such as (cone beam) computed tomography and magnetic
resonance imaging, have proven to be a valuable component for oral and maxillofacial …

Purpose/objective (s) Auto-segmentation with artificial intelligence (AI) offers an opportunity
to reduce inter-and intra-observer variability in contouring, to improve the quality of contours …

Background and purpose To investigate the performance of head-and-neck (HN) organs-at-
risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep …