Deep Learning-Based Registration of MR and Treatment Planning Ultrasound Images for HDR Prostate Brachytherapy

Abstract

Purpose/Objective(s) Propagation of contours from high-quality magnetic resonance (MR) images to treatment planning ultrasound (US) images with severe needle artifacts is a challenging task, which can greatly aid the organ or intraprostatic lesion contouring in high dose rate (HDR) prostate brachytherapy. In this study, a deep learning approach was developed to automatize this registration procedure for HDR brachytherapy practice.

Materials/Methods With the approval from the institutional review board, the MR and US images with manual prostate contours were collected from our clinical database and processed to construct the deep learning dataset. A deep learning framework based on the weakly-supervised strategy was proposed for the registration of prostate MR images and US images after the needle insertion, which consisted of two segmentation networks and a deformable registration network. Specifically, two 3D V-Net was trained for the prostate segmentation on the MR and US images separately, to generate the weak supervision labels automatically for the registration network training. Besides the image pair, the corresponding prostate probability maps from the segmentation were further fed to the registration network to improve the registration accuracy, and the deformation matrix was the output. A random augmentation method was designed to alleviate the map overfitting problem during the network training. The overlap between the deformed and fixed prostate contours was analyzed to evaluate the registration accuracy.

Results Clinical data from 121, 104 and 63 patient cases were collected and utilized for the MR and US image segmentation networks, and the registration network learning, respectively. The mean DSC, center-of-mass (COM) distance, Hausdorff distance (HD) and averaged symmetric surface distance (ASSD) results for the registration of prostate contours were 0.87 ± 0.05, 1.70 ± 0.89 mm, 7.21 ± 2.07 mm, 1.61 ± 0.64 mm, respectively. By providing the prostate probability map from the segmentation to the registration network, as well as applying the random map augmentation method, the evaluation results of the four metrics were all improved, such as an increase of DSC from 0.83 ± 0.08 to 0.86 ± 0.06 and from 0.86 ± 0.06 to 0.87 ± 0.05, respectively.

Conclusion This study first worked on the deep learning-based registration about the treatment planning US image for HDR prostate brachytherapy. A novel segmentation-based registration framework was proposed for this multi-modality registration problem, which not only largely saved the labor work on the data preparation, but also improved the registration accuracy. The evaluation results showed the potential of this approach in HDR brachytherapy to automatize current clinical procedures.