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 …

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 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 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 …

Purpose Contouring inconsistencies are known but understudied in clinical radiation
therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group …

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 …

Purpose We recently described the validation of deep learning-based auto-segmented
contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study …

Along with the improved treatment conformity achieved with the recently implemented
radiotherapy (RT) planning and delivery approaches [1], there is growing awareness of the …

Background and purpose Radiation dose to the cardio-pulmonary system is critical for
radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically …

Background and purpose In radiotherapy, automatic organ-at-risk segmentation algorithms
allow faster delineation times, but clinically relevant contour evaluation remains challenging …