title: Multi-label segmentation of white matter structures: application to neonatal brains.
abstract: 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 algorithm in Riemannian diffusion tensor spaces. Our ML-kNN considers 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.
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore.
authors.forename: Nagulan
authors.initials: N
authors.lastname: Ratnarajah
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore; Clinical Imaging Research Centre, National University of Singapore, Singapore. Electronic address: bieqa@nus.edu.sg.
authors.forename: Anqi
authors.initials: A
authors.lastname: Qiu
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D009412:Nerve Fibers; D066127:White Matter / Q000033:anatomy & histology*
keywords: Diffusion weighted MRI; Multi-label white matter segmentation; Neonatal brain; Riemannian manifold of diffusion tensors