Journal
NEUROIMAGE
Volume 124, Issue -, Pages 770-782Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.07.076
Keywords
Patch matching; Segmentation; Late fusion; Hippocampus; Patch-based method
Funding
- French State
- French National Research Agency (ANR) [ANR-10-IDEX-03-02]
- Cluster of excellence CPU
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Ministerio de Economia y competitividad [TIN2013-43457-R]
- NIH [P30AG010129, K01 AG030514]
- Dana Foundation
- TRAIL (HR-DTI) [ANR-10-LABX-57]
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Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations. (C) 2015 Elsevier Inc. All rights reserved.
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