Globally Optimal Closed-Surface Segmentation for Connectomics

Bjoern Andres¹, Thorben Kroeger¹, Kevin L. Briggman², Winfried Denk³, Natalya Korogod⁴, Graham Knott⁴, Ullrich Koethe¹, and Fred A. Hamprecht¹

¹HCI University of Heidelberg, Germany

²NIH, Bethesda, USA

³MPI for Medical Research, Heidelberg, Germany

⁴EPFL, Lausanne, Switzerland

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.

LNCS 7574, p. 778 ff.

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