The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data
Abstract
It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.667881 · Cited by 5,290
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
Level Set Methods and Fast Marching Methods
Sethian, J. A. (1999) · Medical Entomology and Zoology · Cited by 3,616
Combining Pattern Classifiers: Methods and Algorithms
Bagui, S. C. (2005) · Technometrics · doi:10.1198/tech.2005.s320 · Cited by 3,202
Combining Pattern Classifiers: Methods and Algorithms
Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,005
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
Morphometric analysis of white matter lesions in MR images: method and validation
Zijdenbos, A. P., Dawant, B. M., Margolin, R. A., & Palmer, A. C. (1994) · IEEE Transactions on Medical Imaging · doi:10.1109/42.363096 · Cited by 1,265
Automatic anatomical brain MRI segmentation combining label propagation and decision fusion
Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.05.061 · Cited by 866
Rohlfing, T., Brandt, R., Menzel, R., & Maurer, C. R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.11.010 · Cited by 573
Cited by
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311
Multi-Atlas Segmentation with Joint Label Fusion
Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.143 · Cited by 796
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
Yushkevich, P. A., Pluta, J. B., Wang, H., Xie, L., Ding, S.-L., Gertje, E. C., Mancuso, L., Kliot, D., Das, S. R., & Wolk, D. A. (2015) · Human Brain Mapping · doi:10.1002/hbm.22627 · Cited by 599
Metadata
- Authors
- Artaechevarria, X., Munoz-Barrutia, A., & Ortiz-de-Solorzano, C.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2009-08
- Volume
- 28
- Issue
- 8
- Pages
- 1266-1277
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2128806031
- Cited by
- 507
- References
- 31
- FWCI
- 32.25
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data. https://app.cochat.ai/papers/10.1109/tmi.2009.2014372
- The paper
- Artaechevarria, X., Munoz-Barrutia, A., & Ortiz-de-Solorzano, C. (2009). Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2009.2014372
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
