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Automated Delineation of Dendritic Networks in Noisy Image Stacks

  • Germán González
  • François Fleuret
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

We present a novel approach to 3D delineation of dendritic networks in noisy image stacks. We achieve a level of automation beyond that of state-of-the-art systems, which model dendrites as continuous tubular structures and postulate simple appearance models. Instead, we learn models from the data itself, which make them better suited to handle noise and deviations from expected appearance.

From very little expert-labeled ground truth, we train both a classifier to recognize individual dendrite voxels and a density model to classify segments connecting pairs of points as dendrite-like or not. Given these models, we can then trace the dendritic trees of neurons automatically by enforcing the tree structure of the resulting graph. We will show that our approach performs better than traditional techniques on brighfield image stacks.

Keywords

Gaussian Mixture Model Minimum Span Tree Appearance Model Dendritic Tree Weak Learner 
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 2008

Authors and Affiliations

  • Germán González
    • 1
  • François Fleuret
    • 2
  • Pascal Fua
    • 1
  1. 1.Ecole Polytechnique Fédérale de Lausanne, Computer Vision LaboratoryBâtiment BCSwitzerland
  2. 2.IDIAP Research InstituteMartignySwitzerland

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