Purpose While artificial intelligence has shown great promise in organs-at-risk (OARs) auto
segmentation for head and neck cancer (HNC) radiotherapy, to reach the level of clinical …

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT)
treatment planning workflow. Time-consuming and inter-observer variability are main issues …

Purpose In radiotherapy planning, manual contouring is labor‐intensive and time‐
consuming. Accurate and robust automated segmentation models improve the efficiency …

Introduction Organ-at-risk segmentation for head and neck cancer radiation therapy is a
complex and time-consuming process (requiring up to 42 individual structure, and may …

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 …

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

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 Deep learning-based head and neck lymph node level (HN_LNL)
autodelineation is of high relevance to radiotherapy research and clinical treatment planning …

Purpose Auto‐segmentation algorithms offer a potential solution to eliminate the labor‐
intensive, time‐consuming, and observer‐dependent manual delineation of organs‐at‐risk …

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 …