Automatic segmentation and applicator reconstruction for CT‐based brachytherapy of cervical cancer using 3D convolutional neural networks

Abstract

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, HDs between the channel paths determined automatically and manually were 0.88 ± 0.12 mm, 0.95 ± 0.16 mm, and 0.96 ± 0.15 mm for the intrauterine, left ovoid, and right ovoid tubes, respectively. The proposed DSD‐UNET method outperformed the 3D U‐Net and could segment HR‐CTV, bladder, and rectum with relatively good accuracy. Accurate digitization of the channel paths could be achieved with the DSD‐UNET‐based method. The proposed approaches could be useful to improve the efficiency and consistency of treatment planning for cervical cancer BT.

Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer

“…To the best of our knowledge, there is a limited number of studies reporting on the auto-segmentation of OARs in brachytherapy treatment planning for cervical cancers [40]. The aim of this study is to assess the feasibility of auto-segmentation using a deep learning approach for OARs delineation considering manual contouring as standard of reference.…”

“…Imperfect fixing causes diversity in balloon location, which might affect the results. Zhang et al [40] used DSD-UNET and 3D UNET for automated segmentation of HR-CTV and OARs on 91 CT images of patients with cervical cancers. Our results showed superior performance considering the entire quantitative metrics including DSC, Jaccard, and HD OARs segmentation such as bladder, rectum, and sigmoid, though we used a 2D deep learning architecture.…”

Automatic segmentation of high‐risk clinical target volume for tandem‐and‐ovoids brachytherapy patients using an asymmetric dual‐path convolutional neural network

“…26 Tong et al proposed a multi-task edge-recalibrated network to adaptively enhance its segmentation performance by extracting the edge-related features during training. 16 Meanwhile, very little work has been done to autosegment the HR-CTV for high dose rate brachytherapy, which was considerably more challenging due to the lack of visible anatomical edges in CT. 18,19 GEC-ESTRO recommends performing the "pre-exam" MR scan for tumor size and anatomical evaluation, and applicator selection. With the applicator in situ, the "pretreatment" MR scan is recommended for contouring and treatment planning at each implantation of the applicator.…”

“…Zhang et al. used a UNet to auto‐segment HR‐CTV on CT images alone for T&O patients 19 . However, a potential limitation of this study is that MR was not used.…”

3D-Image-Guided Multi-Catheter Interstitial Brachytherapy for Bulky and High-Risk Stage IIB–IVB Cervical Cancer

“…Hyaluronate gel injection into the perirectal space and vesicovaginal septum could be an effective option for reducing the rectum and bladder dose in ISBT [ 31 , 32 ], although in patients with T4a tumors, this reduction may inhibit adequate dose administration. Finally, recent reports demonstrated that deep learning neural network algorithms using multimodal imaging had the potential for improving the efficiency of OAR contouring [ 33 , 34 ], suggesting improvement in safety of radiation therapy.…”