The truth is hard to make: Validation of medical image registration
Abstract
An unsolved problem in medical image analysis is validation of methods. In this paper we will focus on image registration and in particular on nonlinear image registration, which is one of the hardest analysis problems to validate. The paper covers currently used methods of validation, comparative challenges and public datasets, as well as some of our own work in this area.
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Quantifying uncertainty in registration slowly emerges as a research thrust in 2D/2D and 3D/3D registration.
“Quantifying uncertainty in registration slowly emerges as a research thrust in 2D/2D and 3D/3D registration”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Pluim et al., 2016
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References 39 · Cited by 34
- Authors
- Pluim, J. P. W., Muenzing, S. E. A., Eppenhof, K. A. J., & Murphy, K.
- Affiliations
- University Medical Center Utrecht (NL)
- RWTH Aachen University (DE)
- Eindhoven University of Technology (NL)
- University College Cork (IE)
- Venue
- 2016 23rd International Conference on Pattern Recognition (ICPR)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 2294-2300
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2607997853
- Cited by
- 34
- References
- 39
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 7 · 2020: 4 · 2019: 3 · 2017: 1
- FWCI
- 0.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition (publishedVersion)
- TU/e Research Portal (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- medical image registration, nonlinear image registration, benchmark dataset, comparative analysis, registration accuracy assessment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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