Multi-classifier framework for atlas-based image segmentation
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The ensemble approach efficiently solves the problem of selecting optimal algorithm/parameter values by covering a range of possible values without having to pick one, and provides a robust segmentation tool.
“As stated in Rohlfing and Maurer (2005), the ensemble approach efficiently solves this problem by covering a range of possible values without having to pick one, and in this way provides a robust segmentation tool.”
From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Rohlfing and Maurer (2005)
In Rohlfing and Maurer (2005), classifier combinations consistently improved classification accuracy, and improvements in accuracy were possible with various parameterizations of the non-rigid registration technique, even using a single atlas.
“the authors observed that in all cases classifier combinations consistently improved classification accuracy, and that improvements in accuracy were possible with various parameterizations of the non-rigid registration technique, even using a single atlas.”
From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Rohlfing and Maurer (2005)
Cites
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547 · see citing works
Hill, D. L. G., Batchelor, P. G., Holden, M., & Hawkes, D. J. (2001) · Physics in Medicine and Biology · doi:10.1088/0031-9155/46/3/201 · Cited by 2,093 · see citing works
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Multi-atlas segmentation of biomedical images: A survey
Iglesias, J. E., & Sabuncu, M. R. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.06.012 · Cited by 667 · see citing works
Federated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works
Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., & Davatzikos, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.073 · Cited by 309 · see citing works
“As stated in Rohlfing and Maurer (2005), the ensemble approach efficiently solves this problem by covering a range of possible values without having to pick one, and in this way provides a robust segmentation tool.”
Pfefferbaum, A., Rohlfing, T., Rosenbloom, M. J., Chu, W., Colrain, I. M., & Sullivan, E. V. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.008 · Cited by 256 · see citing works
Pfefferbaum, A., Chanraud, S., Pitel, A.-L., Muller-Oehring, E., Shankaranarayanan, A., Alsop, D. C., Rohlfing, T., & Sullivan, E. V. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq090 · Cited by 104 · see citing works
Self-citationPati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., et al. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9449 · Cited by 51 · see citing works
- Authors
- Rohlfing, T., & Maurer, C. R.
- Affiliations
- Stanford Medicine (US)
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2005-10
- Volume
- 26
- Issue
- 13
- Pages
- 2070-2079
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2130223630
- Cited by
- 80
- References
- 2
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 2 · 2017: 4 · 2016: 4 · 2015: 6 · 2014: 6 · 2013: 5 · 2012: 4
- FWCI
- 6.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition Letters (publishedVersion)
- Funders
- Massachusetts General Hospital
- Stanford Bio-X
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- atlas-based segmentation, classifier combination, image registration, medical image segmentation, classification accuracy
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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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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