Solving Large Multicut Problems for Connectomics via Domain Decomposition

Constantin Pape, Thorsten Beier, Peter Li, Viren Jain, Davi D. Bock, Anna Kreshuk; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1-10

Abstract


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 preva- lent 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 segmenta- tion of the blocks in the hierarchy. We validate the accuracy of our algorithm on a small fully annotated dataset (5x5x5 mm) and demonstrate no significant loss in segmentation quality compared to solving the Multicut problem globally. We evaluate the scalability of the algorithm on a 95x60x60 mm image volume and show that solving the Multicut prob- lem is no longer the bottleneck of the segmentation pipeline.

Related Material


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[bibtex]
@InProceedings{Pape_2017_ICCV,
author = {Pape, Constantin and Beier, Thorsten and Li, Peter and Jain, Viren and Bock, Davi D. and Kreshuk, Anna},
title = {Solving Large Multicut Problems for Connectomics via Domain Decomposition},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}