  The purpose of this study is to develop a deep learning based method that can
automatically generate segmentations on cone-beam CT (CBCT) for head and neck
online adaptive radiation therapy (ART), where expert-drawn contours in
planning CT (pCT) can serve as prior knowledge. Due to lots of artifacts and
truncations on CBCT, we propose to utilize a learning based deformable image
registration method and contour propagation to get updated contours on CBCT.
Our method takes CBCT and pCT as inputs, and output deformation vector field
and synthetic CT (sCT) at the same time by jointly training a CycleGAN model
and 5-cascaded Voxelmorph model together.The CycleGAN serves to generate sCT
from CBCT, while the 5-cascaded Voxelmorph serves to warp pCT to sCT's
anatommy. The segmentation results were compared to Elastix, Voxelmorph and
5-cascaded Voxelmorph on 18 structures including left brachial plexus, right
brachial plexus, brainstem, oral cavity, middle pharyngeal constrictor,
superior pharyngeal constrictor, inferior pharyngeal constrictor, esophagus,
nodal gross tumor volume, larynx, mandible, left masseter, right masseter, left
parotid gland, right parotid gland, left submandibular gland, right
submandibular gland, and spinal cord. Results show that our proposed method can
achieve average Dice similarity coefficients and 95% Hausdorff distance of 0.83
and 2.01mm. As compared to other methods, our method has shown better accuracy
to Voxelmorph and 5-cascaded Voxelmorph, and comparable accuracy to Elastix but
much higher efficiency. The proposed method can rapidly and simultaneously
generate sCT with correct CT numbers and propagate contours from pCT to CBCT
for online ART re-planning.
