The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit
detail from large datasets have attracted substantial research attention in the field of medical …

Introduction Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN
radiotherapy and for investigating the relationships between radiation dose to OARs and …

Background Due to the large variability in the prostate gland of different patient groups,
manual segmentation is time-consuming and subject to inter-and intra-reader variations …

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 …

Introduction Contouring organs at risk (OARs) is a time‐intensive task that is a critical part of
radiation therapy. Atlas‐based automatic segmentation has shown some success at …

Prostate radiotherapy is a well established curative oncology modality, which in future will
use Magnetic Resonance Imaging (MRI)-based radiotherapy for daily adaptive radiotherapy …

Proper delineation of both target volumes and organs at risk is a crucial step in the radiation
therapy workflow. This process is normally carried out manually by medical doctors, hence …

Purpose Deep learning‐based networks have become increasingly popular in the field of
medical image segmentation. The purpose of this research was to develop and optimize a …

3-D radiotherapy is an effective treatment modality for breast cancer. In 3-D radiotherapy,
delineation of the clinical target volume (CTV) is an essential step in the establishment of …

Accurate clinical target volume (CTV) delineation is important for head and neck intensity-
modulated radiation therapy. However, delineation is time-consuming and susceptible to …