AutomaticSegmentationofNeonatalBrainMRI-theUNC.pptVIP

Automatic Segmentation of Neonatal Brain MRI Marcel Prastawa1, John Gilmore2, Weili Lin3, Guido Gerig1,2 University of North Carolina at Chapel Hill 1Department of Computer Science 2Department of Psychiatry 3Department of Radiology Partially supported by NIH Conte Center MH064065 and NIH-NIBIB R01 EB000219 Goal Segmentation of brain tissues of newborn infants from multimodal MRI Particular interest in the developing white matter structure Motivation: Analysis of growth patterns, study of neuro-developmental disorders starting at a very early age Imaging the Developing Brain Challenges Smaller head size Low contrast-to-noise ratio Intensity inhomogeneity Motion artifacts Division of white matter into myelinated and non-myelinated regions Previous work: Warfield et al 1998 (methodology) Hüppi et al 1998 (clinical study) Challenges Approach Non-optimal input data, rely on high level prior knowledge Intensity ordering (e.g. in T2W) wm-myelinated gm wm-non-myelinated csf Aligned spatial priors (brain atlas) White matter is considered as one entity Method Overview Intensity Clustering Samples obtained by thresholding atlas priors T1 T2 Pr(wm, x) Overlay Noisy data, low contrast ? robust techniques Two robust estimation techniques: Minimum Spanning Tree (MST) clustering Minimum Covariance Determinant (MCD) estimator Obtain initial estimates of intensity distributions Minimum Spanning Tree Clustering [Cocosco et al 2003] Break long edges in MST, example: Detect multiple clusters while pruning outliers Iterative process, stops when cluster feature locations are in the desired order “Feature location” = summary value of cluster intensities Determining Feature Locations Need reliable location estimate to find good clusters Standard estimates (e.g., mean, median) not always optimal Use robust estimator to determine location of a compact point set in a cluster Minimum Covariance Determinant [Rousseeuw et al