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In this contribution we demonstrate how a Multicut-based segmentation pipeline can be scaled up to datasets of hundreds of Gigabytes in size. Such datasets are prevalent in connectomics, where neuron segmentation needs to be performed across very large electron microscopy image volumes. We show the advantages of a hierarchical block-wise scheme over local stitching strategies and evaluate the performance of different Multicut solvers for the segmentation of the blocks in the hierarchy. We validate the accuracy of our algorithm on a small fully annotated dataset (5×5×5 μm) and demonstrate no significant loss in segmentation quality compared to solving the Multicut problem globally. We evaluate the scalability of the algorithm on a 95 x 60 x 60 μm image volume and show that solving the Multicut problem is no longer the bottleneck of the segmentation pipeline.

In this paper we introduce a novel algorithm for alignment of Electron Microscopy images for 3D reconstruction. The algorithm extends the Expectation Maximization - Iterative Closest Points (EM-ICP) algorithm to go from point matching to patch matching. We utilize local patch characteristics to achieve improved registration. The method is applied to enable 3D reconstruction of Transmission Electron Microscopy (TEM) images. We demonstrate results on large TEM images and show the increased alignment accuracy of our approach.