A deep learning-based clinical target volume segmentation in female pelvic MRI for radiation therapy planning

Brachytherapy (BT) combined with external beam radiotherapy (EBRT) is the standard treatment for cervical cancer. Accurate segmentation of the tumor and nearby organs at risk (OAR) is necessary for accurate radiotherapy (RT) planning. While OAR segmentation has been widely studied, showing promising performance, accurate tumor and/or corresponding clinical target volume (CTV) segmentation has been less explored. In cervical cancer RT, magnetic resonance (MR) imaging is used as the standard imaging modality to define the CTV, which is very challenging as the microscopic spread of tumor cells is not clearly visible even in MRI. We propose a two-step convolutional neural network (CNN) approach to delineate CTV from T2-weighted (T2W) MR images. First, a human expert needs to select a seed point inside the CTV region, from which the MR volume is cropped to produce a region of interest (ROI) volume. The ROI volume is then fed to an attention U-Net to produce CTV segmentation. A total of 213 MR datasets from 125 patients was used to develop and evaluate the proposed methodology. The network was trained using 2-dimensional (2-D) slices extracted in the axial direction from 183 MR datasets and augmented using translation operation. The proposed method was tested on the remaining 30 MR datasets and yielded Mean±SD dice similarity coefficient (DSC) of 0.80±0.06 and Hausdorff distance (95th percentile) of 3.30±0.58 mm. The performance of our method is superior to the standard U-Net-based method (pvalue<0.005). Although the proposed method is semi-automatic, the observer variability coefficient of variation (CV) was reported as 2.86% that demonstrated the high reproducibility of the algorithm.