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

Abstract

Purposes: Preimplant diagnostic magnetic resonance imaging is the gold standard for image-guided tandem-and-ovoids (T&O) brachytherapy for cervical cancer. However, high dose rate brachytherapy planning is typically done on postimplant CT-based high-risk clinical target volume (HR-CTV_CT ) because the transfer of preimplant Magnetic resonance (MR)-based HR-CTV (HR-CTV_MR ) to the postimplant planning CT is difficult due to anatomical changes caused by applicator insertion, vaginal packing, and the filling status of the bladder and rectum. This study aims to train a dual-path convolutional neural network (CNN) for automatic segmentation of HR-CTV_CT on postimplant planning CT with guidance from preimplant diagnostic MR.

Methods: Preimplant T2-weighted MR and postimplant CT images for 65 (48 for training, eight for validation, and nine for testing) patients were retrospectively solicited from our institutional database. MR was aligned to the corresponding CT using rigid registration. HR-CTV_CT and HR-CTV_MR were manually contoured on CT and MR by an experienced radiation oncologist. All images were then resampled to a spatial resolution of 0.5 × 0.5 × 1.25 mm. A dual-path 3D asymmetric CNN architecture with two encoding paths was built to extract CT and MR image features. The MR was masked by HR-CTV_MR contour while the entire CT volume was included. The network put an asymmetric weighting of 18:6 for CT: MR. Voxel-based dice similarity coefficient (DSC_V ), sensitivity, precision, and 95% Hausdorff distance (95-HD) were used to evaluate model performance. Cross-validation was performed to assess model stability. The study cohort was divided into a small tumor group (<20 cc), medium tumor group (20-40 cc), and large tumor group (>40 cc) based on the HR-CTV_CT for model evaluation. Single-path CNN models were trained with the same parameters as those in dual-path models.

Results: For this patient cohort, the dual-path CNN model improved each of our objective findings, including DSC_V , sensitivity, and precision, with an average improvement of 8%, 7%, and 12%, respectively. The 95-HD was improved by an average of 1.65 mm compared to the single-path model with only CT images as input. In addition, the area under the curve for different networks was 0.86 (dual-path with CT and MR) and 0.80 (single-path with CT), respectively. The dual-path CNN model with asymmetric weighting achieved the best performance with DSC_V of 0.65 ± 0.03 (0.61-0.70), 0.79 ± 0.02 (0.74-0.85), and 0.75 ± 0.04 (0.68-0.79) for small, medium, and large group. 95-HD were 7.34 (5.35-10.45) mm, 5.48 (3.21-8.43) mm, and 6.21 (5.34-9.32) mm for the three size groups, respectively.

Conclusions: An asymmetric CNN model with two encoding paths from preimplant MR (masked by HR-CTV_MR ) and postimplant CT images was successfully developed for automatic segmentation of HR-CTV_CT for T&O brachytherapy patients.

Keywords: CNN; Deep learning; HDR; Tandem and Ovoids; high-risk CTV; segmentation.

FIGURE 1

Axial, sagittal, and coronal views of computed tomography (CT) and MR for a representative patient, which show the comparison of location and size of preimplant MR-based high-risk clinical target volume (HR-CTV_MR) and postimplant planning-CT-based HR-CTV_CT

FIGURE 2

The architecture of the asymmetric convolutional neural network (CNN) model. Each blue cuboid corresponds to a feature map. The number of channels is denoted on the top of the cuboid

FIGURE 3

(a) The volume distribution of high-risk clinical target volume (HR-CTV_CT) and HR-CTV_MR. (b) Boxplots of HR-CTV_CT in computed tomography (CT) and HR-CTV_MR in MR. The difference in volumes is not statistically significant (p = 0.06)

FIGURE 4

(a) Receiver operating characteristic (ROC) curves from the four models, including dual-path with asymmetric weighting (DP_AW), dual-path with symmetric weighting (DP_SW), single-path with input computed tomography (CT) + MR (SP_CTMR), and single-path with input CT (SP_CT). (b) The objective loss as a function of the epoch for the four convolutional neural network (CNN) models. (c) The objective loss as a function of epoch for training and validation (DP_AW_VAL) cohorts for dual path with asymmetric weighting

FIGURE 5

Comparison of two additional radiation oncologists (RO1 and RO2), the primary brachytherapy radiation oncologist (PRO), and our model

FIGURE 6

Comparison of manual PRO (red), RO1 (pink), RO2 (purple), and automatic dual-path (green) contours in axial, sagittal, and coronal views for a representative patient