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Adaptive Neonate Brain Segmentation

  • M. Jorge Cardoso
  • Andrew Melbourne
  • Giles S. Kendall
  • Marc Modat
  • Cornelia F. Hagmann
  • Nicola J. Robertson
  • Neil Marlow
  • Sebastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Babies born prematurely are at increased risk of adverse neurodevelopmental outcomes. Recent advances suggest that measurement of brain volumes can help in defining biomarkers for neurodevelopmental outcome. These techniques rely on an accurate segmentation of the MRI data. However, due to lack of contrast, partial volume (PV) effect, the existence of both hypo- and hyper-intensities and significant natural and pathological anatomical variability, the segmentation of neonatal brain MRI is challenging. We propose a pipeline for image segmentation that uses a novel multi-model Maximum a posteriori Expectation Maximisation (MAP-EM) segmentation algorithm with a prior over both intensities and the tissue proportions, a B0 inhomogeneity correction, and a spatial homogeneity term through the use of a Markov Random Field. This robust and adaptive technique enables the segmentation of images with high anatomical disparity from a normal population. Furthermore, the proposed method implicitly models Partial Volume, mitigating the problem of neonatal white/grey matter intensity inversion. Experiments performed on a clinical cohort show expected statistically significant correlations with gestational age at birth and birthweight. Furthermore, the proposed method obtains statistically significant improvements in Dice scores when compared to the a Maximum Likelihood EM algorithm.

Keywords

Markov Random Field Maximum Likelihood Expectation Maximisation Dice Score Adverse Neurodevelopmental Outcome Tissue Proportion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Jorge Cardoso
    • 1
  • Andrew Melbourne
    • 1
  • Giles S. Kendall
    • 2
  • Marc Modat
    • 1
  • Cornelia F. Hagmann
    • 2
  • Nicola J. Robertson
    • 2
  • Neil Marlow
    • 2
  • Sebastien Ourselin
    • 1
  1. 1.Centre for Medical Image Computing (CMIC)University College LondonUK
  2. 2.Academic NeonatologyEGA UCL Institute for Women’s HealthLondonUK

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