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 …

Purpose We recently described the validation of deep learning-based auto-segmented
contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study …

Purpose Little is known about the scale of clinical implementation of automated treatment
planning techniques in the United States. In this work, we examine the barriers and …

In this study, we evaluate the performance of the Segment Anything Model (SAM) model in
clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic …

Manual segmentation is the gold standard method for radiation therapy planning; however, it
is time-consuming and prone to inter-and intra-observer variation, giving rise to interests in …

Background Radiation treatment is considered an effective and the most common treatment
option for prostate cancer. The treatment planning process requires accurate and precise …

Background Impaired function of masticatory muscles will lead to trismus. Routine
delineation of these muscles during planning may improve dose tracking and facilitate dose …

Background and purpose Head and neck (HN) radiotherapy can benefit from automatic
delineation of tumor and surrounding organs because of the complex anatomy and the …

Background Contour delineation, a crucial process in radiation oncology, is time-consuming
and inaccurate due to inter-observer variation has been a critical issue in this process. An …

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 …