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. © 2012 Springer-Verlag.
CITATION STYLE
Andres, B., Kroeger, T., Briggman, K. L., Denk, W., Korogod, N., Knott, G., … Hamprecht, F. A. (2012). Globally optimal closed-surface segmentation for connectomics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7574 LNCS, pp. 778–791). https://doi.org/10.1007/978-3-642-33712-3_56
Mendeley helps you to discover research relevant for your work.