Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
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Other metrics not uniquely attributable to a domain include registration capture range and registration success rate.
“Other metrics that are not uniquely attributable to a domain include the registration capture range (Schaffert et al., 2019; Esfandiari et al., 2021) and the registration success rate (Varnavas et al., 2013; Mitrovi et al., 2014; Miao et al., 2016b; Yang and Chen, 2019; Schaffert et al., 2020a).”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Schaffert et al., 2019
Methods that model 2D/3D correspondences will require an additional dimension to assign points to objects, adding complexity and potentially increasing training challenges.
“Methods which model 2D/3D correspondences (Schaffert et al., 2019, 2020a,b; Liao et al., 2019) will require an additional dimension to handle the assignment of points to various obejcts, adding complexity and potentially increasing the challenges associated with training.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Schaffert et al., 2019
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- Authors
- Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., & Maier, A.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Siemens Healthineers (Germany) (DE)
- Corresponding author
- Schaffert, Roman
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 140-152
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2809152687
- Cited by
- 19
- References
- 18
- Citations by year
- 2024: 3 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 8 · 2019: 2
- FWCI
- 1.05
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- rigid registration, PointNet, projection error, end-to-end learning, minimally invasive surgery, registration robustness
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