  Purpose: Segmentation of organs-at-risk (OARs) is a bottleneck in current
radiation oncology pipelines and is often time consuming and labor intensive.
In this paper, we propose an atlas-based semi-supervised registration algorithm
to generate accurate segmentations of OARs for which there are ground truth
contours and rough segmentations of all other OARs in the atlas. To the best of
our knowledge, this is the first study to use learning-based registration
methods for the segmentation of head and neck patients and demonstrate its
utility in clinical applications. Methods: Our algorithm cascades rigid and
deformable deformation blocks, and takes on an atlas image (M), set of
atlas-space segmentations (S_A), and a patient image (F) as inputs, while
outputting patient-space segmentations of all OARs defined on the atlas. We
train our model on 475 CT images taken from public archives and Stanford RadOnc
Clinic (SROC), validate on 5 CT images from SROC, and test our model on 20 CT
images from SROC. Results: Our method outperforms current state of the art
learning-based registration algorithms and achieves an overall dice score of
0.789 on our test set. Moreover, our method yields a performance comparable to
manual segmentation and supervised segmentation, while solving a much more
complex registration problem. Whereas supervised segmentation methods only
automate the segmentation process for a select few number of OARs, we
demonstrate that our methods can achieve similar performance for OARs of
interest, while also providing segmentations for every other OAR on the
provided atlas. Conclusions: Our proposed algorithm has significant clinical
applications and could help reduce the bottleneck for segmentation of head and
neck OARs. Further, our results demonstrate that semi-supervised diffeomorphic
registration can be accurately applied to both registration and segmentation
problems.
