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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)


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.


Volume Image Rand Index Video Segmentation Integer Linear Program Problem Correlation Cluster 
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 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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