期刊
MEDICAL IMAGE ANALYSIS
卷 44, 期 -, 页码 143-155出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2017.11.013
关键词
Patch-based label fusion; Multi-atlas segmentation; Neural networks; Embedding; Brain MRI; Hippocampus
类别
资金
- Marie Curie FP7-PEOPLE-COFUND Action [600387]
- Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme [MDM-2015-0502]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant) [U01 AG024904]
- DOD ADNI (Department of Defense award) [W81XWH-12-2-0012]
- NATIONAL INSTITUTE ON AGING [U01AG024904, K01AG049089] Funding Source: NIH RePORTER
In brain structural segmentation, multi-atlas strategies are increasingly being used over single-atlas strategies because of their ability to fit a wider anatomical variability. Patch-based label fusion (PBLF) is a type of such multi-atlas approaches that labels each target point as a weighted combination of neighboring atlas labels, where atlas points with higher local similarity to the target contribute more strongly to label fusion. PBLF can be potentially improved by increasing the discriminative capabilities of the local image similarity measurements. We propose a framework to compute patch embeddings using neural networks so as to increase discriminative abilities of similarity-based weighted voting in PBLF. As particular cases, our framework includes embeddings with different complexities, namely, a simple scaling, an affine transformation, and non-linear transformations. We compare our method with state-of-the-art alternatives in whole hippocampus and hippocampal subfields segmentation experiments using publicly available datasets. Results show that even the simplest versions of our method outperform standard PBLF, thus evidencing the benefits of discriminative learning. More complex transformation models tended to achieve better results than simpler ones, obtaining a considerable increase in average Dice score compared to standard PBLF. (C) 2017 Elsevier B.V. All rights reserved.
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