  We implemented and evaluated a multiple resolution residual network (MRRN)
for multiple normal organs-at-risk (OAR) segmentation from computed tomography
(CT) images for thoracic radiotherapy treatment (RT) planning. Our approach
simultaneously combines feature streams computed at multiple image resolutions
and feature levels through residual connections. The feature streams at each
level are updated as the images are passed through various feature levels. We
trained our approach using 206 thoracic CT scans of lung cancer patients with
35 scans held out for validation to segment the left and right lungs, heart,
esophagus, and spinal cord. This approach was tested on 60 CT scans from the
open-source AAPM Thoracic Auto-Segmentation Challenge dataset. Performance was
measured using the Dice Similarity Coefficient (DSC). Our approach outperformed
the best-performing method in the grand challenge for hard-to-segment
structures like the esophagus and achieved comparable results for all other
structures. Median DSC using our method was 0.97 (interquartile range [IQR]:
0.97-0.98) for the left and right lungs, 0.93 (IQR: 0.93-0.95) for the heart,
0.78 (IQR: 0.76-0.80) for the esophagus, and 0.88 (IQR: 0.86-0.89) for the
spinal cord.
