Biomechanically constrained non-rigid mr-trus prostate registration using deep learning based 3d point cloud matching
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A non-rigid MR-TRUS image registration framework is proposed for prostate interventions. The registration framework consists of a convolutional neural networks (CNN) for MR prostate segmentation, a CNN for TRUS prostate segmentation and a point-cloud based network for rapid 3D point cloud matching. Volumetric prostate point clouds were generated from the segmented prostate masks using tetrahedron meshing. The point cloud matching network was trained using deformation field that was generated by finite element analysis. Therefore, the network implicitly models the underlying biomechanical constraint when performing point cloud matching. A total of 50 patients' datasets were used for the network training and testing. Alignment of prostate shapes after registration was evaluated using three metrics including Dice similarity coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD). Internal point-to-point registration accuracy was assessed using target registration error (TRE). Jacobian determinant and strain tensors of the predicted deformation field were calculated to analyze the physical fidelity of the deformation field. On average, the mean and standard deviation were 0.94±0.02, 0.90±0.23 mm, 2.96±1.00 mm and 1.57±0.77 mm for DSC, MSD, HD and TRE, respectively. Robustness of our method to point cloud noise was evaluated by adding different levels of noise to the query point clouds. Our results demonstrated that the proposed method could rapidly perform MR-TRUS image registration with good registration accuracy and robustness.
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Fu et al. designed an MR-TRUS registration network for prostate interventions; the supervision deformation field is generated from population-based FE models from point clouds with biomechanical constraints.
“Recently, Fu et al. (75) designed an MR-TRUS registration network for prostate interventions; the supervision deformation field is generated from population-based FE models from point clouds with biomechanical constraints.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [75]
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- Authors
- Fu, Y., Lei, Y., Wang, T., Patel, P., Jani, A. B., Mao, H., Curran, W. J., Liu, T., & Yang, X.
- Affiliations
- Emory University (US)
- Corresponding author
- Yang, Xiaofeng
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 67
- Pages
- 101845
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC7725979
- OpenAlex
- W3092614481
- Cited by
- 75
- References
- 74
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 13 · 2023: 15 · 2022: 11 · 2021: 8 · 2020: 1
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Medical Image Analysis (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- DOD Prostate Cancer Research Program
- National Cancer Institute
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- prostate, deformable image registration, point cloud matching, finite element analysis, deformation field, prostate segmentation, convolutional neural networks
- MeSH terms
- Deep Learning, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Prostatic Neoplasms, Ultrasonography
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