期刊
NEUROIMAGE
卷 102, 期 -, 页码 913-922出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2014.08.001
关键词
Multi-label white matter segmentation; Neonatal brain; Diffusion weighted MRI; Riemannian manifold of diffusion tensors
资金
- National Medical Research Council (NMRC) [NMRC/TCR/012-NUHS/2014, NMRC/CBRG/0039/2013]
- National University of Singapore [NUSYIA FY10 P07]
- Singapore Ministry of Education Academic Research Fund Tier 2 [MOE2012-T2-2-130]
Accurate and consistent segmentation of brain white matter bundles at neonatal stage plays an important role in understanding brain development and detecting white matter abnormalities for the prediction of psychiatric disorders. Due to the complexity of white matter anatomy and the spatial resolution of diffusion-weighted MR imaging, multiple fiber bundles can pass through one voxel. The goal of this study is to assign one or multiple anatomical labels of white matter bundles to each voxel to reflect complex white matter anatomy of the neonatal brain. For this, we develop a supervised multi-label k-nearest neighbor (ML-kNN) classification algorithmin Riemannian diffusion tensor spaces. OurML-kNNconsiders diffusion tensors lying on the Log-Euclidean Riemannian manifold of symmetric positive definite (SPD) matrices and their corresponding vector space as feature space. The ML-kNN utilizes the maximum a posteriori (MAP) principle to make the prediction of white matter labels by reasoning with the labeling information derived from the neighbors without assuming any probabilistic distribution of the features. We show that our approach automatically learns the number of white matter bundles at a location and provides anatomical annotation of the neonatal white matter. In addition, our approach also provides the binary mask for individual white matter bundles to facilitate tract-based statistical analysis in clinical studies. We apply this method to automatically segment 13 white matter bundles of the neonatal brain and examine the segmentation accuracy against semi-manual labels derived from tractography. (C) 2014 Elsevier Inc. All rights reserved.
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