Automatic segmentation of MR images of the developing newborn brain

Marcel Prastawa, John H. Gilmore, Weili Lin, Guido Gerig

    Research output: Contribution to journalArticlepeer-review


    This paper describes an automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI). The analysis and study of newborn brain MRI is of great interest due to its potential for studying early growth patterns and morphological changes in neurodevelopmental disorders. Automatic segmentation of newborn MRI is a challenging task mainly due to the low intensity contrast and the growth process of the white matter tissue. Newborn white matter tissue undergoes a rapid myelination process, where the nerves are covered in myelin sheathes. It is necessary to identify the white matter tissue as myelinated or non-myelinated regions. The degree of myelination is a fractional voxel property that represents regional changes of white matter as a function of age. Our method makes use of a registered probabilistic brain atlas. The method first uses robust graph clustering and parameter estimation to find the initial intensity distributions. The distribution estimates are then used together with the spatial priors to perform bias correction. Finally, the method refines the segmentation using training sample pruning and non-parametric kernel density estimation. Our results demonstrate that the method is able to segment the brain tissue and identify myelinated and non-myelinated white matter regions.

    Original languageEnglish (US)
    Pages (from-to)457-466
    Number of pages10
    JournalMedical Image Analysis
    Issue number5 SPEC. ISS.
    StatePublished - Oct 2005


    • Automatic brain MRI classification
    • Automatic brain MRI segmentation
    • Early brain development
    • Kernel density estimation
    • Neonatal MRI
    • Robust estimation

    ASJC Scopus subject areas

    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Computer Vision and Pattern Recognition
    • Health Informatics
    • Computer Graphics and Computer-Aided Design


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