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Automatic Atlas-Based Segmentation of Brain White Matter in Neonates at Risk for Neurodevelopmental Disorders

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Modeling, Analysis, and Visualization of Anisotropy

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Very preterm infants, < 32 weeks gestation, are at high risk for brain injury. Cognitive deficits are often diagnosed at a later stage, since there are no available predictive biomarkers in the neonatal period. The maturation of specific white matter (WM) brain structures is considered a promising early-stage biomarker. With Diffusion Tensor Imaging (DTI ) tractography , an in vivo and non-invasive evaluation of these anatomical structures is possible.

We developed an automatic tractography segmentation pipeline, which allows for maturation assessment of the different segmented WM structures. Our segmentation pipeline is atlas-based , specifically designed for premature neonates at term equivalent age. In order to better make use of global information from tractography , all processing is done in the fiber domain. Segmented fiber bundles are further automatically quantified with respect to volume and anisotropy. Of the 24 automatically segmented neonatal tractographies, only three contained more than 30% mislabeled fibers. Results show no dependency to WM pathology. By automatically segmenting WM, we reduced the user-dependency and bias characteristic of manual methods. This study assesses the structure of the neonatal brain based on an automatic WM segmentation in the fiber domain method using DTI tractography data.

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Notes

  1. 1.

    http://vistalab.stanford.edu/.

  2. 2.

    http://sourceforge.net/projects/viste/.

  3. 3.

    http://www.fil.ion.ucl.ac.uk/spm/software/spm8/.

  4. 4.

    http://sourceforge.net/projects/viste/.

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Acknowledgements

We are grateful to Floris Groenendaal, Linda de Vries, and Manon Benders, that we are granted the re-use of the fiber atlas that part of our group developed in a previous study together with the Department of Neonatology, Wilhelmina Children’s Hospital/University Medical Center Utrecht. In addition, we thank Lauren O’Donnell for providing us with their fiber registration software.

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Authors and Affiliations

  1. Medical Image Analysis, Eindhoven University of Technology, Eindhoven, The Netherlands

    L. Fonseca, R. van den Boom & A. Vilanova

  2. Clinical Physics, Máxima Medical Center, Veldhoven, The Netherlands

    C. van Pul

  3. School of Medical Physics and Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    C. van Pul

  4. LANEN, INECO, INCYT (Favaloro-CONICET), Rosario, Argentina

    N. Lori

  5. Centre Algoritmi, University of Minho, Braga, Portugal

    N. Lori

  6. Neonatology Maxima Medical Center, Veldhoven, The Netherlands

    P. Andriessen & J. Buijs

  7. Computer Graphics and Visualization, Delft University of Technology, Delft, The Netherlands

    A. Vilanova

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  1. L. Fonseca
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  2. C. van Pul
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  3. N. Lori
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  4. R. van den Boom
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  6. J. Buijs
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  7. A. Vilanova
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Corresponding author

Correspondence to A. Vilanova .

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Editors and Affiliations

  1. Institute of Computer Science, University of Bonn, Bonn, Germany

    Thomas Schultz

  2. Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden

    Evren Özarslan

  3. Department of Science & Technology, Linköping University, Norrköping, Sweden

    Ingrid Hotz

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Fonseca, L. et al. (2017). Automatic Atlas-Based Segmentation of Brain White Matter in Neonates at Risk for Neurodevelopmental Disorders. In: Schultz, T., Özarslan, E., Hotz, I. (eds) Modeling, Analysis, and Visualization of Anisotropy. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-61358-1_15

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