Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery
Intra-operative target pose estimation is fundamental in minimally invasive surgery (MIS) to guiding surgical robots. This task can be fulfilled by the 2-D/3-D rigid registration, which aligns the anatomical structures between intra-operative 2-D fluoroscopy and the pre-operative 3-D computed tomogr...
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MDPI AG
2021-07-01
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| Online Access: | https://www.mdpi.com/2072-666X/12/7/844 |
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| author | Zhou An Honghai Ma Lilu Liu Yue Wang Haojian Lu Chunlin Zhou Rong Xiong Jian Hu |
| author_facet | Zhou An Honghai Ma Lilu Liu Yue Wang Haojian Lu Chunlin Zhou Rong Xiong Jian Hu |
| author_sort | Zhou An |
| collection | DOAJ |
| description | Intra-operative target pose estimation is fundamental in minimally invasive surgery (MIS) to guiding surgical robots. This task can be fulfilled by the 2-D/3-D rigid registration, which aligns the anatomical structures between intra-operative 2-D fluoroscopy and the pre-operative 3-D computed tomography (CT) with annotated target information. Although this technique has been researched for decades, it is still challenging to achieve accuracy, robustness and efficiency simultaneously. In this paper, a novel orthogonal-view 2-D/3-D rigid registration framework is proposed which combines the dense reconstruction based on deep learning and the GPU-accelerated 3-D/3-D rigid registration. First, we employ the X2CT-GAN to reconstruct a target CT from two orthogonal fluoroscopy images. After that, the generated target CT and pre-operative CT are input into the 3-D/3-D rigid registration part, which potentially needs a few iterations to converge the global optima. For further efficiency improvement, we make the 3-D/3-D registration algorithm parallel and apply a GPU to accelerate this part. For evaluation, a novel tool is employed to preprocess the public head CT dataset CQ500 and a CT-DRR dataset is presented as the benchmark. The proposed method achieves 1.65 ± 1.41 mm in mean target registration error(mTRE), 20% in the gross failure rate(GFR) and 1.8 s in running time. Our method outperforms the state-of-the-art methods in most test cases. It is promising to apply the proposed method in localization and nano manipulation of micro surgical robot for highly precise MIS. |
| first_indexed | 2024-03-10T09:31:19Z |
| format | Article |
| id | doaj.art-3fadaa4f7207424f9b272aecca33b27b |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T09:31:19Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-3fadaa4f7207424f9b272aecca33b27b2023-11-22T04:25:23ZengMDPI AGMicromachines2072-666X2021-07-0112784410.3390/mi12070844Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive SurgeryZhou An0Honghai Ma1Lilu Liu2Yue Wang3Haojian Lu4Chunlin Zhou5Rong Xiong6Jian Hu7Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, ChinaState Key Laboratory of Industrial Control and Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Industrial Control and Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Industrial Control and Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Industrial Control and Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Industrial Control and Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, ChinaIntra-operative target pose estimation is fundamental in minimally invasive surgery (MIS) to guiding surgical robots. This task can be fulfilled by the 2-D/3-D rigid registration, which aligns the anatomical structures between intra-operative 2-D fluoroscopy and the pre-operative 3-D computed tomography (CT) with annotated target information. Although this technique has been researched for decades, it is still challenging to achieve accuracy, robustness and efficiency simultaneously. In this paper, a novel orthogonal-view 2-D/3-D rigid registration framework is proposed which combines the dense reconstruction based on deep learning and the GPU-accelerated 3-D/3-D rigid registration. First, we employ the X2CT-GAN to reconstruct a target CT from two orthogonal fluoroscopy images. After that, the generated target CT and pre-operative CT are input into the 3-D/3-D rigid registration part, which potentially needs a few iterations to converge the global optima. For further efficiency improvement, we make the 3-D/3-D registration algorithm parallel and apply a GPU to accelerate this part. For evaluation, a novel tool is employed to preprocess the public head CT dataset CQ500 and a CT-DRR dataset is presented as the benchmark. The proposed method achieves 1.65 ± 1.41 mm in mean target registration error(mTRE), 20% in the gross failure rate(GFR) and 1.8 s in running time. Our method outperforms the state-of-the-art methods in most test cases. It is promising to apply the proposed method in localization and nano manipulation of micro surgical robot for highly precise MIS.https://www.mdpi.com/2072-666X/12/7/8442-D/3-D registrationrigidmulti-viewreconstructiondeep learning |
| spellingShingle | Zhou An Honghai Ma Lilu Liu Yue Wang Haojian Lu Chunlin Zhou Rong Xiong Jian Hu Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery Micromachines 2-D/3-D registration rigid multi-view reconstruction deep learning |
| title | Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery |
| title_full | Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery |
| title_fullStr | Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery |
| title_full_unstemmed | Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery |
| title_short | Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery |
| title_sort | robust orthogonal view 2 d 3 d rigid registration for minimally invasive surgery |
| topic | 2-D/3-D registration rigid multi-view reconstruction deep learning |
| url | https://www.mdpi.com/2072-666X/12/7/844 |
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