Globally optimal closed-surface segmentation for connectomics

Authors:
Bjoern Andres;Thorben Kroeger;Kevin L. Briggman;Winfried Denk;Natalya Korogod;Graham Knott;Ullrich Koethe;Fred A. Hamprecht
Affiliations:
HCI University of Heidelberg, Germany;HCI University of Heidelberg, Germany;NIH, Bethesda;MPI for Medical Research, Heidelberg, Germany;EPFL, Lausanne, Switzerland;EPFL, Lausanne, Switzerland;HCI University of Heidelberg, Germany;HCI University of Heidelberg, Germany
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Year:
2012

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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.