Dr. Wei Wei
Publications
2021
Wei, W; Haishan, X; Alpers, J; Rak, M; Hansen, C
A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation Journal Article
In: Computer Methods and Programs in Biomedicine, vol. 206, pp. 106117, 2021, ISSN: 0169-2607.
@article{wei_deep_2021,
title = {A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation},
author = {W Wei and X Haishan and J Alpers and M Rak and C Hansen},
url = {https://www.sciencedirect.com/science/article/pii/S0169260721001929},
doi = {10.1016/j.cmpb.2021.106117},
issn = {0169-2607},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
journal = {Computer Methods and Programs in Biomedicine},
volume = {206},
pages = {106117},
abstract = {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° 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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° 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.
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° 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.
2020
Wei, W; Rak, M; Alpers, J; Hansen, C
Towards Fully Automatic 2D Us to 3D CT/MR Registration: A Novel Segmentation-Based Strategy Journal Article
In: pp. 433–437, 2020, ISBN: 978-1-5386-9330-8.
@article{wei_towards_2020,
title = {Towards Fully Automatic 2D Us to 3D CT/MR Registration: A Novel Segmentation-Based Strategy},
author = {W Wei and M Rak and J Alpers and C Hansen},
url = {https://ieeexplore.ieee.org/document/9098379/},
doi = {10.1109/ISBI45749.2020.9098379},
isbn = {978-1-5386-9330-8},
year = {2020},
date = {2020-04-01},
urldate = {2020-04-01},
booktitle = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
pages = {433–437},
publisher = {IEEE},
address = {Iowa City, IA, USA},
abstract = {2D-US to 3D-CT/MR registration is a crucial module during minimally invasive ultrasound-guided liver tumor ablations. Many modern registration methods still require manual or semi-automatic slice pose initialization due to insufficient robustness of automatic methods. The state-of-the-art regression networks do not work well for liver 2D US to 3D CT/MR registration because of the tremendous inter-patient variability of the liver anatomy. To address this unsolved problem, we propose a deep learning network pipeline which instead of a regression starts with a classification network to recognize the coarse ultrasound transducer pose followed by a segmentation network to detect the target plane of the US image in the CT/MR volume. The rigid registration result is derived using plane regression. In contrast to the state-of-the-art regression networks, we do not estimate registration parameters from multi-modal images directly, but rather focus on segmenting the target slice plane in the volume. The experiments reveal that this novel registration strategy can identify the initial slice phase in a 3D volume more reliably than the standard regression-based techniques. The proposed method was evaluated with 1035 US images from 52 patients. We achieved angle and distance errors of 12.7 ± 6.2 degrees and 4.9 ± 3.1 mm, clearly outperforming the state-of-the-art regression strategy which results in 37.0 ± 15.6 degrees angle error and 19.0 ± 11.6 mm distance error.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2D-US to 3D-CT/MR registration is a crucial module during minimally invasive ultrasound-guided liver tumor ablations. Many modern registration methods still require manual or semi-automatic slice pose initialization due to insufficient robustness of automatic methods. The state-of-the-art regression networks do not work well for liver 2D US to 3D CT/MR registration because of the tremendous inter-patient variability of the liver anatomy. To address this unsolved problem, we propose a deep learning network pipeline which instead of a regression starts with a classification network to recognize the coarse ultrasound transducer pose followed by a segmentation network to detect the target plane of the US image in the CT/MR volume. The rigid registration result is derived using plane regression. In contrast to the state-of-the-art regression networks, we do not estimate registration parameters from multi-modal images directly, but rather focus on segmenting the target slice plane in the volume. The experiments reveal that this novel registration strategy can identify the initial slice phase in a 3D volume more reliably than the standard regression-based techniques. The proposed method was evaluated with 1035 US images from 52 patients. We achieved angle and distance errors of 12.7 ± 6.2 degrees and 4.9 ± 3.1 mm, clearly outperforming the state-of-the-art regression strategy which results in 37.0 ± 15.6 degrees angle error and 19.0 ± 11.6 mm distance error.
2019
Wei, W; Xu, H; Alpers, J; Tianbao, Z; Wang, L; Rak, M; Hansen, C
Fast Registration for Liver Motion Compensation in Ultrasound-Guided Navigation Journal Article
In: pp. 1132–1136, 2019, ISBN: 978-1-5386-3641-1.
@article{wei_fast_2019,
title = {Fast Registration for Liver Motion Compensation in Ultrasound-Guided Navigation},
author = {W Wei and H Xu and J Alpers and Z Tianbao and L Wang and M Rak and C Hansen},
url = {https://ieeexplore.ieee.org/document/8759464/},
doi = {10.1109/ISBI.2019.8759464},
isbn = {978-1-5386-3641-1},
year = {2019},
date = {2019-04-01},
urldate = {2019-04-01},
booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
pages = {1132–1136},
publisher = {IEEE},
address = {Venice, Italy},
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 UNet 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 = {},
pubstate = {published},
tppubtype = {article}
}
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 UNet 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.