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 …

Background: In this study, a deep convolutional neural network (CNN)-based automatic
segmentation technique was applied to multiple organs at risk (OARs) depicted in computed …

Purpose This report presents the methods and results of the Thoracic Auto‐Segmentation
Challenge organized at the 2017 Annual Meeting of American Association of Physicists in …

This study aimed to examine the efficacy of semantic segmentation implemented by deep
learning and to confirm whether this method is more effective than a commercially dominant …

Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing
trend in medical imaging and become the state-of-the-art method in various clinical …

Segmentation of organs and structures, as either targets or organs‐at‐risk, has a significant
influence on the success of radiation therapy. Manual segmentation is a tedious and time …

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 …

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 …

Lung cancer presents one of the leading causes of mortalities for people around the world.
Lung image analysis and segmentation are one of the primary steps used for early …

To investigate the impact of training sample size on the performance of deep learning-based
organ auto-segmentation for head-and-neck cancer patients, a total of 1160 patients with …