MDA-TransUNet: A Deep Learning-Based Automatic Segmentation Method for Cervical Cancer Brachytherapy.

IntroductionAccurate delineation of the high-risk clinical target volume (HR-CTV) and organs at risk (OARs) is critical for cervical cancer brachytherapy. However, treatment planning is time-consuming, and prolonged waiting can lead to organ displacement and patient discomfort. Additionally, the steep dose gradients around HR-CTV amplify segmentation errors in HR-CTV and OARs. Therefore, achieving rapid and precise delineation of HR-CTV and OARs remains challenging. This study proposes a novel network model, MDA-TransUNet, for fast segmentation of HR-CTV and OARs in cervical cancer.MethodsWe applied MDA-TransUnet, a CNN-Transformer hybrid model, to segment the bladder, colon, rectum, small bowel, and HR-CTV on cervical cancer CT images. 122 cervical cancer brachytherapy patients' CT images from three clinical centers were utilized for training and testing, with 80 cases allocated to training, 22 to testing, and 20 to external validation. Segmentation accuracy was quantified using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95), and Average Surface Distance (ASD). Dosimetric differences were analyzed via paired t-tests.ResultsCompared to other methods, MDA-TransUnet achieved superior segmentation performance on the test dataset. The DSCs for the bladder, colon, rectum, small bowel, and HR-CTV were 94.54%, 79.27%, 79.27%, 88.90%, and 82.35%, respectively. Paired t-tests on five dosimetric metrics (D5cc, D2cc, D0.1cc, D90%, and Dmean) showed no significant differences. For OARs, the average difference in D2cc was less than 12%. For HR-CTV, the average difference in Dmean was less than 8%, and D90% was less than 11%.ConclusionThis work demonstrates the superiority of MDA-TransUnet in segmenting OARs and HR-CTV for cervical cancer brachytherapy, with robust performance across multi-center datasets.