@INPROCEEDINGS{Wei_2019_ISBI,
  author={Wei, W. and Xu, H. and Alpers, J. and Tianbao, Z. and Wang, L. and Rak, M. and Hansen, C.},
  booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, 
  title={Fast Registration for Liver Motion Compensation in Ultrasound-Guided Navigation}, 
  year={2019},
  volume={},
  number={},
  pages={1132-1136},
  abstract={In recent years, image-guided thermal ablations have become a considerable treatment method for cancer patients, including support through navigational systems. One of the most critical challenges in these systems is the registration between the intraoperative images and the preoperative volume. The motion secondary to inspiration makes registration even more difficult. In this work, we propose a coarse-fine fast patient registration technique to solve the problem of motion compensation. In contrast to other state-of-the-art methods, we focus on improving the convergence range of registration. To this end, we make use of a Deep Learning 2D U-Net framework to extract the vessels and liver borders from intraoperative ultrasound images and employ the segmentation results as regions of interest in the registration. After an initial 3D-3D registration during breath hold, the following motion compensation is achieved using a 2D-3D registration. Our approach yields a convergence rate of over 70% with an accuracy of 1.97+1.07 mm regarding the target registration error. The 2D-3D registration is GPU-accelerated with a time cost of less than 200 ms.},
  keywords={biomedical ultrasonics;cancer;image registration;image segmentation;learning (artificial intelligence);liver;medical image processing;motion compensation;fast registration;liver motion compensation;ultrasound-guided navigation;image-guided thermal ablations;cancer patients;intraoperative images;liver borders;intraoperative ultrasound images;target registration error;coarse-fine fast patient registration;deep learning 2D U-Net framework;ultrasound image segmentation;initial 3D-3D registration;2D-3D registration;convergence rate;GPU-acceleration;Computed tomography;Liver;Two dimensional displays;Image segmentation;Three-dimensional displays;Ultrasonic imaging;Probes;U-Net;CMA-ES;CUDA;Registration},
  doi={10.1109/ISBI.2019.8759464},
  ISSN={1945-8452},
  month={April},}