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Genus Zero Graph Segmentation: Estimation of Intracranial Volume

  • Rasmus R. Jensen
  • Signe S. Thorup
  • Rasmus R. Paulsen
  • Tron A. Darvann
  • Nuno V. Hermann
  • Per Larsen
  • Sven Kreiborg
  • Rasmus Larsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

The intracranial volume (ICV) in children with premature fusion of one or more sutures in the calvaria is of interest due to the risk of increased intracranial pressure. Challenges for automatic estimation of ICV include holes in the skull e.g. the foramen magnum and fontanelles. In this paper, we present a fully automatic 3D graph-based method for segmentation of the ICV in non-contrast CT scans. We reformulate the ICV segmentation problem as an optimal genus 0 segmentation problem in a volumetric graph. The graph is the result of a volumetric spherical subsample from the data connected using Delaunay tetrahedralisation. A Markov Random Field is constructed on the graph with probabilities learned from an Expectation Maximisation algorithm matching a Mixture of Gaussians to the data. Results are compared to manual segmentations performed by an expert. We have achieved very high Dice scores ranging from 98.14% to 99.00%, while volume deviation from the manual segmentation ranges from 0.7%-3.7%. The Hausdorff distance, which shows the maximum error from automatic to manual segmentation ranges, from 4.73-9.81mm. Since this is sensitive to single error, we have also found the 95% Hausdorff distance, which ranges from 1.10-3.65mm. The proposed method is expected to perform well for other volumetric segmentations.

Keywords

Intracranial volume CT craniosynostosis graph cut segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rasmus R. Jensen
    • 1
  • Signe S. Thorup
    • 1
  • Rasmus R. Paulsen
    • 1
  • Tron A. Darvann
    • 2
    • 3
  • Nuno V. Hermann
    • 2
    • 4
  • Per Larsen
    • 2
  • Sven Kreiborg
    • 2
    • 4
    • 5
  • Rasmus Larsen
    • 1
  1. 1.DTU ComputeTechnical University of DenmarkLyngbyDenmark
  2. 2.3D Craniofacial Image Research LaboratoryUniversity of CopenhagenCopenhagenDenmark
  3. 3.Dept. of Oral and Maxillofacial SurgeryCopenhagen University HospitalCopenhagenDenmark
  4. 4.Pediatric Dentistry and Clinical GeneticsUniversity of CopenhagenCopenhagenDenmark
  5. 5.Dept. of Clinical GeneticsCopenhagen University HospitalCopenhagenDenmark

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