Multi-Atlas Segmentation with Joint Label Fusion
Open accessAbstract
Multi-atlas segmentation is an effective approach for automatically labeling objects of interest in biomedical images. In this approach, multiple expert-segmented example images, called atlases, are registered to a target image, and deformed atlas segmentations are combined using label fusion. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity have been particularly successful. However, one limitation of these strategies is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this limitation, we propose a new solution for the label fusion problem in which weighted voting is formulated in terms of minimizing the total expectation of labeling error and in which pairwise dependency between atlases is explicitly modeled as the joint probability of two atlases making a segmentation error at a voxel. This probability is approximated using intensity similarity between a pair of atlases and the target image in the neighborhood of each voxel. We validate our method in two medical image segmentation problems: hippocampus segmentation and hippocampus subfield segmentation in magnetic resonance (MR) images. For both problems, we show consistent and significant improvement over label fusion strategies that assign atlas weights independently.
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- Authors
- Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A.
- Affiliations
- University of Pennsylvania (US)
- HeartFlow (United States) (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-03
- Volume
- 35
- Issue
- 3
- Pages
- 611-623
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- PMC
- PMC3864549
- OpenAlex
- W2148157540
- Cited by
- 796
- References
- 48
- Citations by year
- 2026: 20 · 2025: 35 · 2024: 31 · 2023: 30 · 2022: 50 · 2021: 68 · 2020: 69 · 2019: 97 · 2018: 88 · 2017: 83 · 2016: 84 · 2015: 77 · 2014: 39 · 2013: 20 · 2012: 5
- FWCI
- 46.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Alzheimer's Association (U01 AG024904)
- Bristol-Myers Squibb (U01 AG024904, AG024904)
- Eli Lilly and Company (U01 AG024904, AG024904)
- Pfizer (AG024904, U01 AG024904)
- AstraZeneca
- GlaxoSmithKline
- Novartis
- University of Pennsylvania
- F. Hoffmann-La Roche (AG024904, U01 AG024904)
- Medpace (U01 AG024904)
- Synarc (U01 AG024904)
- Alzheimer's Disease Neuroimaging Initiative (U01AG024904, AG024904)
- Elan (U01 AG024904)
- Eisai (U01 AG024904, AG024904)
- National Institutes of Health (5u01ag024904-10, U01-AG024904, RO1 AG010897, AG024904)
- U.S. Food and Drug Administration
- Genentech (AG024904, U01 AG024904)
- National Institute on Aging (U01-AG-024904, P30AG010129, AG 024904, K01AG030514, R01AG037376, R01AG010897, K25AG027785)
- 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
- multi-atlas segmentation, label fusion, weighted voting, hippocampus segmentation, hippocampal subfield segmentation, magnetic resonance imaging, medical image segmentation
- MeSH terms
- Algorithms, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Databases, Factual
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