Globally Optimal Closed-Surface Segmentation for Connectomics

  • Bjoern Andres
  • Thorben Kroeger
  • Kevin L. Briggman
  • Winfried Denk
  • Natalya Korogod
  • Graham Knott
  • Ullrich Koethe
  • Fred A. Hamprecht
Conference paper

DOI: 10.1007/978-3-642-33712-3_56

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)
Cite this paper as:
Andres B. et al. (2012) Globally Optimal Closed-Surface Segmentation for Connectomics. In: Fitzgibbon A., Lazebnik S., Perona P., Sato Y., Schmid C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7574. Springer, Berlin, Heidelberg

Abstract

We address the problem of partitioning a volume image into a previously unknown number of segments, based on a likelihood of merging adjacent supervoxels. Towards this goal, we adapt a higher-order probabilistic graphical model that makes the duality between supervoxels and their joint faces explicit and ensures that merging decisions are consistent and surfaces of final segments are closed. First, we propose a practical cutting-plane approach to solve the MAP inference problem to global optimality despite its NP-hardness. Second, we apply this approach to challenging large-scale 3D segmentation problems for neural circuit reconstruction (Connectomics), demonstrating the advantage of this higher-order model over independent decisions and finite-order approximations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bjoern Andres
    • 1
  • Thorben Kroeger
    • 1
  • Kevin L. Briggman
    • 2
  • Winfried Denk
    • 3
  • Natalya Korogod
    • 4
  • Graham Knott
    • 4
  • Ullrich Koethe
    • 1
  • Fred A. Hamprecht
    • 1
  1. 1.HCI University of HeidelbergGermany
  2. 2.NIHBethesdaUSA
  3. 3.MPI for Medical ResearchHeidelbergGermany
  4. 4.EPFLLausanneSwitzerland

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