The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A Generative Model for Image Segmentation Based on Label Fusion
Open accessAbstract
We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans-with manually segmented white matter, cerebral cortex, ventricles and subcortical structures-to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.
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Cites
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Self-citationA fast diffeomorphic image registration algorithm
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Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment
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Cited by
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
Self-citationWithin-subject template estimation for unbiased longitudinal image analysis
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Self-citationMulti-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
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“…with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al., 2014; Wu et al., 2015).”
Metadata
- Authors
- Sabuncu, M. R., Yeo, B. T. T., Van Leemput, K., Fischl, B., & Golland, P.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2010-10
- Volume
- 29
- Issue
- 10
- Pages
- 1714-1729
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2102595307
- Cited by
- 479
- References
- 13
- FWCI
- 43.12
- Type
- journal article
- Language
- en
- Open access
- green
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