Synthetic CT-based multi-organ segmentation in cone beam CT for adaptive pancreatic radiotherapy

The inter-fraction motion management of pancreatic radiotherapy remains a challenge in current clinical practice. CBCT-based adaptive radiotherapy is an emerging technique for either offline or online plan adaptations. Accurately delineating tumor targets and organs-at-risk (OARs) is an important step in adaptive re-planning process; however, manual delineation can be labor-intensive and time-consuming. Especially for online adaptation, rapid re-planning is generally required. In this study, we present a fully automated delineation method to expedite the contouring process of adaptive radiotherapy re-planning and dose-volume based plan evaluation and monitoring. In particular, to avoid scatter artifact from CBCT and improve the image quality, a cycle-consistent adversarial network was firstly used to generate synthetic CT given CBCT. Then, a mask scoring regional neural network (RCNN) has been developed to extract the features from synthetic CT for obtaining final segmentation. Metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance (MSD), and residual mean square distance (RMS) were used to evaluate our proposed method. Overall, DSC values ranging from 0.82 to 0.94 were achieved among 8 organs.