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 …

Radiation therapy is one of the most widely used therapies for cancer treatment. A critical
step in radiation therapy planning is to accurately delineate all organs at risk (OARs) to …

Purpose Manual delineation of organs‐at‐risk (OAR s) in radiotherapy is both time‐
consuming and subjective. Automated and more accurate segmentation is of the utmost …

Purpose Automatic segmentation of organs‐at‐risk (OAR s) is a key step in radiation
treatment planning to reduce human efforts and bias. Deep convolutional neural networks …

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 …

Purpose We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation
model that can provide accurate and consistent OARs segmentation results in much less …

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

Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and
high‐quality radiation therapy planning. The purpose of this work is to develop a deep …

Computed Tomography (CT) has been widely used in the planning of radiation therapy,
which is one of the most effective clinical lung cancer treatment options. Accurate …

Purpose Accurate segmentation of organs‐at‐risks (OAR s) is the key step for efficient
planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we …