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Adaptive radiotherapy has been proposed to handle with the inter- and intra-fractional uncertainties of patient motion and anatomical changes, in which, treatment re-planning is in general a necessary procedure. Accelerated re-planning will improve the efficiency of adaptive radiotherapy, especially for those on-line adaptation. Cone beam CT (CBCT) is a widely used in-room imaging for patient positioning for modern radiotherapy. 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 head and neck (HN) cancer cases, it could highly prolong the entire adaptational procedure due to the multiple OARs have to be contoured. In this study, we present a fully automated delineation method with a high quality of performance to expedite the contouring process of adaptive radiotherapy re-planning and dose-volume based plan evaluation and monitoring. In particular, a mask scoring regional neural network has been developed to extract the complementary features from CBCT and synthetic MRI for obtaining final segmentation. The synthetic MRI is generated by using a pre-trained cycle-consistent adversarial network given CBCT. Quantitative 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 of 0.67±0.18, 0.62±0.19, 0.86±0.08, 0.87±0.13, 0.58±0.25, 0.64±0.17, 0.66±0.17, 0.93±0.05, 0.85±0.09, 0.86±0.10, 0.72±0.21, and 0.83±0.07 were achieved for brain stem, esophagus, larynx, mandible, optic chiasm, left optic nerve, right optic nerve, oral cavity, left parotid, right parotid, pharynx, and spinal cord, respectively.
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