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Journal Article

Learning cellular morphology with neural networks

MPS-Authors
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Schubert,  Philipp J.
Department: Electrons-Photons-Neurons / Denk, MPI of Neurobiology, Max Planck Society;

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Dorkenwald,  Sven
Department: Electrons-Photons-Neurons / Denk, MPI of Neurobiology, Max Planck Society;

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Kornfeld,  Jörgen
Department: Electrons-Photons-Neurons / Denk, MPI of Neurobiology, Max Planck Society;

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s41467-019-10836-3.pdf
(Publisher version), 3MB

Supplementary Material (public)

41467_2019_10836_MOESM1_ESM.pdf
(Supplementary material), 2MB

41467_2019_10836_MOESM2_ESM.pdf
(Supplementary material), 205KB

Citation

Schubert, P. J., Dorkenwald, S., Januszewski, M., Jain, V., & Kornfeld, J. (2019). Learning cellular morphology with neural networks. Nature Communications, 10: 2736. doi:10.1038/s41467-019-10836-3.


Cite as: https://hdl.handle.net/21.11116/0000-0005-BE19-A
Abstract
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings (Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm, which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.