Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.
In this study, we present deep learning-based approaches to automatic segmentation and applicator reconstruction with high accuracy and efficiency in the planning computed tomography (CT) for cervical cancer brachytherapy (BT). A novel three-dimensional (3D) convolutional neural network (CNN) architecture was proposed and referred to as DSD-UNET. The dataset of 91 patients received CT-based BT of cervical cancer was used to train and test DSD-UNET model for auto-segmentation of high-risk clinical target volume (HR-CTV) and organs at risk (OARs). Automatic applicator reconstruction was achieved with DSD-UNET-based segmentation of applicator components followed by 3D skeletonization and polynomial curve fitting. Digitization of the channel paths for tandem and ovoid applicator in the planning CT was evaluated utilizing the data from 32 patients. Dice similarity coefficient (DSC), Jaccard Index (JI), and Hausdorff distance (HD) were used to quantitatively evaluate the accuracy. The segmentation performance of DSD-UNET was compared with that of 3D U-Net. Results showed that DSD-UNET method outperformed 3D U-Net on segmentations of all the structures. The mean DSC values of DSD-UNET method were 86.9%, 82.9%, and 82.1% for bladder, HR-CTV, and rectum, respectively. For the performance of automatic applicator reconstruction, outstanding segmentation accuracy was first achieved for the intrauterine and ovoid tubes (average DSC value of 92.1%, average HD value of 2.3 mm). Finally...
CONCLUSIONS: This study showed minimal change in balloon volumes, balloons/T+O positioning, or in radiation dose to bladder and rectum when the applicator remained overnight. These findings support that inflatable vaginal immobilization balloons remaining in situ overnight for additional HDR T+O treatments on sequential days, is safe and provides stable dosimetry.PMID:34122567 | PMC:PMC8170519 | DOI:10.5114/jcb.2021.106117