A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation

Background and Objective: Liver tumor ablation is often guided by ultrasound (US). Due to poor image quality, intraoperative US is fused with preoperative computed tomography or magnetic tomography (CT/MR) images to provide visual guidance. As of today, the underlying 2D US to 3D CT/MR registration problem remains a very challenging task. Methods: We propose a novel pipeline to address this registration problem. Contrary to previous work, we do not formulate the problem as a regression task, which for the given registration problem achieves a low performance regarding accuracy and robustness due to the limited US soft-tissue contrast and the inter-patient variability on liver vessels. Instead, we first estimate the US probe angle roughly by using a classification network. Given this coarse initialization, we then improve the registration by formulation of the problem as a segmentation task, estimating the US plane in the 3D CT/MR through segmentation. Results: We benchmark our approach on 1035 clinical images from 52 patients, yielding average registration errors of 11.6 degrees and 4.7 mm, which outperforms the state of the art SVR method[1]. Conclusion: Our results show the efficiency of the proposed registration pipeline, which has potential to improve the robustness and accuracy of intraoperative patient registration. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a novel approach to address the registration problem between ultrasound and computed tomography/magnetic resonance imaging in liver tumor ablation procedures. By estimating the ultrasound probe angle roughly and improving the registration through segmentation, the proposed method outperforms traditional approaches in terms of accuracy and robustness.