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Globally Optimal Closed-Surface Segmentation for ConnectomicsBjoern 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. lncs@springer.com
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