Abstract

This study explored deep learning-based patient-specific auto-segmentation using transfer learning on daily kVCT images to facilitate adaptive radiotherapy, based on data from the first group of patients treated with the innovative RefleXion system.For head and neck (HaN) site and pelvic site, a deep convolutional segmentation network was initially trained on a population dataset, which contained 67 and 56 patient cases respectively. Then the pre-trained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning CT and 5-26 sets of daily RefleXion kVCT were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric impacts resulting from different auto-segmentation and registration methods were also investigated.The proposed patient-specific network achieved mean DSC results of 0.88 for three HaN organs at risk (OARs) of interest and 0.90 for eight pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour.Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiotherapy.

View details for DOI 10.1016/j.ijrobp.2023.04.026

View details for PubMedID 37141982