Adaptive Auto-Segmentation for MRI-Guided Online Adaptive Radiotherapy of Cervical Carcinoma

Abstract

Accurate and efficient delineation of organs and targets on session images is critical in MRI-guided online adaptive radiotherapy (MRgOART). This study proposes a registration-guided deep learning image segmentation framework to assist online delineation of cervical carcinoma. A total of 300 T2-weighted MR images were acquired for patients with cervical carcinoma treated by a 1.5T Unity MR-Linac. The CTV, bladder, rectum, pelvic bone and femoral joints were delineated on each MRI by the same radiation oncologist. To overcome these obstacles to online MRI segmentation, we propose a registration-guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. Firstly, the DL segmentation model was trained using nnU-net. Then, for each treatment fraction, the deformable image registration (DIR) algorithm generates initial contours from previous treatment fraction, which were used as guidance by DL model to obtain the accurate current segmentation. The segmentation accuracy of alone DIR, DL and RgDL were evaluated by dice similarity coefficients (DSC) and other distance-based metrics. Compared to the baseline approaches using the DIR and the DL alone, RgDL achieved a DSC of 91.12% on CTV, higher than DIR and DL alone by 15.54% and 10.13%. The DSC of RgDL were improved to 95.58%, 93.65%, 87.8% and 94.84% for bladder, pelvic bone, rectum and femoral joints, higher than DIR and DL alone by 9.61% on average. The proposed adaptive auto-segmentation method can achieve accurate and efficient segmentation and potentially overcome these obstacles to MRgOART.