Cited by 19Medical Image Segmentation Techniques3D Shape Modeling and AnalysisMedical Imaging and Analysis

Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration

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Schaffert, Roman, Wang, Jian, Fischer, Peter, Borsdorf, Anja, Maier, Andreas·2019·Lecture Notes in Computer Science

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Crossref · 10.1007/978-3-030-12939-2_11

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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).”
  • 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.”

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  1. [12]12. Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., Maier, A.: Multi-View DepthAware Rigid 2-D/3-D Registration. In: IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (2017)

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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
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
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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