Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning.,Journal of Applied Clinical Medical Physics

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Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning.
Journal of Applied Clinical Medical Physics ( IF 2.0 ) Pub Date : 2020-06-29 , DOI: 10.1002/acm2.12871
Charles C Vu _{1,

2} , Zaid A Siddiqui _{1,

2} , Leonid Zamdborg _{1,

2} , Andrew B Thompson _{1,

2} , Thomas J Quinn _{1,

2} , Edward Castillo ₂ , Thomas M Guerrero _{1,

2}

Affiliation

Segmentation of organs‐at‐risk (OARs) is an essential component of the radiation oncology workflow. Commonly segmented thoracic OARs include the heart, esophagus, spinal cord, and lungs. This study evaluated a convolutional neural network (CNN) for automatic segmentation of these OARs.

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