Purpose To investigate the accuracy and robustness of prostate segmentation using deep
learning across various training data sizes, MRI vendors, prostate zones, and testing …

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 …

Genitourinary (GU) system is among the most commonly involved malignancy sites in the
human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease …

Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. The accurate and automatic
segmentation of computed tomography (CT) images of organs at risk (OAR) is clinically …

Deep learning advanced to one of the most important technologies in almost all medical
fields. Especially in areas, related to medical imaging it plays a big role. However, in …

Objective Automated methods for prostate segmentation on MRI are typically developed
under ideal scanning and anatomical conditions. This study evaluates three different …

Background It is necessary to contour regions of interest (ROIs) for online magnetic
resonance imaging (MRI)‐guided adaptive radiotherapy (MRIgART). These updated …

Введение. Определение границ предстательной железы является начальным шагом в
понимании состояния органа и в основном выполняется вручную, что занимает …

Background. Delineation of the prostate boundaries represents the initial step in
understanding the state of the whole organ and is mainly manually performed, which takes a …