CycleSeg-v2: Improving unpaired MR-to-CT Synthesis and Segmentation with pseudo label and LPIPS loss

Radiotherapy treatment typically requires both CT and MRI as well as labor intensive contouring for effective planning and treatment. Deep learning can enable an MR-only workflow by generating synthetic CT (sCT) and performing automatic segmentation on the MR data. However, MR and CT data are usually unpaired and limited contours are available for MR data. In this study, we proposed CycleSeg-v2 that extends the previously proposed CycleSeg to work with unpaired data. To ensure robust training, we employed LPIPS loss in addition to pseudo label. Experiments with data from prostate cancer patients showed that CycleSeg-v2 improved upon previous approaches.