  A large amount of manual segmentation is typically required to train a robust
segmentation network so that it can segment objects of interest in a new
imaging modality. The manual efforts can be alleviated if the manual
segmentation in one imaging modality (e.g., CT) can be utilized to train a
segmentation network in another imaging modality (e.g., CBCT/MRI/PET). In this
work, we developed an anatomy-constrained contrastive synthetic segmentation
network (AccSeg-Net) to train a segmentation network for a target imaging
modality without using its ground truth. Specifically, we proposed to use
anatomy-constraint and patch contrastive learning to ensure the anatomy
fidelity during the unsupervised adaptation, such that the segmentation network
can be trained on the adapted image with correct anatomical structure/content.
The training data for our AccSeg-Net consists of 1) imaging data paired with
segmentation ground-truth in source modality, and 2) unpaired source and target
modality imaging data. We demonstrated successful applications on CBCT, MRI,
and PET imaging data, and showed superior segmentation performances as compared
to previous methods.
