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 In breast cancer patients receiving radiotherapy (RT), accurate target
delineation and reduction of radiation doses to the nearby normal organs is important …

Background and purpose Delineating organs at risk (OARs) on computed tomography (CT)
images is an essential step in radiation therapy; however, it is notoriously time-consuming …

Objectives Atlas-based automatic segmentation (ABAS) addresses the challenges of
accuracy and reliability in manual segmentation. We aim to evaluate the contribution of …

Purpose To study the performance of a proposed deep learning-based autocontouring
system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts …

Background and purpose Auto-segmentation represents an efficient tool to segment organs
on CT imaging. Primarily used in clinical setting, auto-segmentation plays an increasing role …

Background Accurate and standardized descriptions of organs at risk (OARs) are essential
in radiation therapy for treatment planning and evaluation. Traditionally, physicians have …

Background and purpose To internally and externally validate an atlas based automated
segmentation (ABAS) in loco-regional radiation therapy of breast cancer. Materials and …

Manual segmentation of target structures and organs at risk is a crucial step in the
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …

Lung cancer is the leading cause of cancer-related mortality for males and females.
Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While …