Segmentation of subcortical structures and the hippocampus in brain MRI using graph-cuts and subject-specific a-priori information
Abstract
We propose a general framework for segmentation of subcortical structures and the hippocampus in magnetic resonance brain images based on multi-atlas label propagation and graph cuts. The label maps obtained from multi-atlas segmentation are used to build a subject-specific probabilistic atlas of a structure of interest. From this atlas and an intensity model estimated from the unseen image, a Markov random field-based energy function is defined and optimized via graph cuts. Compared to a previously proposed approach, our method does not rely on manual training of the intensity model and is applied to five subcortical structures and the hippocampus. We used this approach to segment the hippocampus on 60 images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and achieved an average overlap (Dice coefficient) of 0.86 with the manually delineated reference segmentations.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Estimating the intensity distribution of ROIs from the target image is more robust to global and local intensity variations between the atlases and the target image than estimating from atlas images.
“we estimate the intensity distribution of ROIs from the target image, similar to Wolz et al. (2009). Such estimation is more robust to global and local intensity variations between the atlases and the target image.”
From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Wolz et al. (2009)
Cited by
Sorted by citations
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
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
“It's important to note that in contrast to methods where the intensity model is estimated from the atlas images for which the segmentation labels are known, we estimate the intensity distribution of ROIs from the target image, similar to Wolz et al. (2009).”
Wang, H., Das, S. R., Suh, J. W., Altinay, M., Pluta, J., Craige, C., Avants, B., & Yushkevich, P. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.006 · Cited by 177 · see citing works
Pluta, J., Yushkevich, P., Das, S., & Wolk, D. (2012) · Journal of Alzheimer's Disease · doi:10.3233/jad-2012-111931 · Cited by 107 · see citing works
- Authors
- Wolz, R., Aljabar, P., Rueckert, D., Heckemann, R. A., & Hammers, A.
- Affiliations
- Imperial College London (GB)
- Venue
- 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 470-473
- OpenAlex
- W2156564892
- Cited by
- 28
- References
- 0
- Citations by year
- 2025: 1 · 2023: 1 · 2019: 3 · 2016: 2 · 2015: 5 · 2014: 2 · 2013: 2 · 2012: 2
- FWCI
- 3.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- hippocampus segmentation, subcortical structures, brain MRI segmentation, graph cuts, intensity models, Alzheimer's disease, ADNI
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
