MR-to-CT synthesis for cross-modality model adaptation

Segmentation models in medical imaging that are able to segment a wide variety of structures and generalize on different image data is a relevant and recent research topic. With more and more universal segmentation models for both MR and CT being released recently a trend towards generalizing segmentation models for large amounts of structures can be observed. While universal MR segmentation models provide segmentations for a wide variety of structures, other structures are limited to models trained on CT data. One such model is TotalSegmentator which is able to segment up to 117 structures. In our work we present and evaluate a method to leverage models trained on CT data like the TotalSegmentator model for MRI data by training a structure-consistent CycleGAN on unpaired and unregistered data. We demonstrate the feasibility of using domain transfer by leveraging unlabeled and unpaired MR and CT datasets from various scanners and sites, with different sequences and protocols from the public AMOS22 abdomen dataset. This approach translates MR to CT contrast, allowing the synthetic CT image to be used as input for the TotalSegmentator model. Furthermore, we evaluate the segmentation accuracy of our approach on different structure types such as organs, muscles and bones on internal MR and CT datasets and compare them to the recently released TotalSegmentatorMRI and MRSegmentator models.