Daily cone-beam CT multi-organ segmentation for prostate adaptive radiotherapy

Adaptive radiotherapy which relies on patient Cone-Beam CT (CBCT) imaged right before dose delivery to update the contouring and treatment plans has the potential to increase dose delivery accuracy and improve treatment outcomes. In this study, we proposed a synthetic MRI-aided multi-organ segmentation from cone-beam CT for prostate adaptive radiotherapy. A cycle-consistent generative adversarial network (CycleGAN) was first trained with pre-aligned CBCT and MRI image pairs to generate sMRI based on given CBCT image. Feature maps were then extracted from CBCT and sMRI separately using two different U-Nets. The feature maps were combined using attention gates and input to CNN to predict the final segmentation of these critical structures. The segmentation results were evaluated using 100 patients’ datasets. The Dice similarity coefficient (DSC) was 0.96±0.03, 0.91±0.08, 0.93±0.04, 0.95±0.05, and 0.95±0.05 for bladder, prostate, rectum, left femoral head (LFH) and right femoral head (RFH), respectively.