A Deep Structured Learning Approach Towards Automating Connectome Reconstruction from 3D Electron Micrographs

Abstract

We present a deep learning method for neuron segmentation from 3D electron microscopy (EM), which improves significantly upon state of the art in terms of accuracy and scalability. Our method consists of a fully 3D extension of the U-NET architecture, trained to predict affinity graphs on voxels, followed by a simple and efficient iterative region agglomeration. We train the U-NET using a structured loss function based on MALIS that encourages topological correctness. The resulting affinity predictions are accurate enough that we obtain state-of-theart results by a simple new learning-free percentile-based iterative agglomeration algorithm. We demonstrate the accuracy of our method on three different and diverse EM datasets where we significantly improve over the current state of the art. We also show for the first time that a common 3D segmentation strategy can be applied to both well-aligned nearly isotropic block-face EM data, and poorly aligned anisotropic serial sectioned EM data. The runtime of our method scales with O(n) in the size of the volume and is thus ready to be applied to very large datasets.

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Cite this paper

@article{Funke2017ADS, title={A Deep Structured Learning Approach Towards Automating Connectome Reconstruction from 3D Electron Micrographs}, author={Jan Funke and Fabian Tschopp and William Grisaitis and Arlo Sheridan and Chandan Singh and Stephan Saalfeld and Srinivas C. Turaga}, journal={CoRR}, year={2017}, volume={abs/1709.02974} }