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Multi-Label Segmentation White Matter Structures: Application to Neonatal Brains.

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dc.contributor.author Nagulan, R.
dc.contributor.author Qiu, A.
dc.date.accessioned 2019-10-22T09:12:34Z
dc.date.accessioned 2022-06-27T04:11:19Z
dc.date.available 2019-10-22T09:12:34Z
dc.date.available 2022-06-27T04:11:19Z
dc.date.issued 2014-11-15
dc.identifier.uri http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1263
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher ElSEVIER/NeuroImage en_US
dc.subject Multi-label white matter segmentation en_US
dc.subject Neonatal brain en_US
dc.subject Diffusion weighted MRI en_US
dc.subject Riemannian manifold of diffusion tensors en_US
dc.title Multi-Label Segmentation White Matter Structures: Application to Neonatal Brains. en_US
dc.type Article en_US


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