Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 73,456 bioRxiv papers from 319,721 authors.

Learning cellular morphology with neural networks

By Philipp J Schubert, Sven Dorkenwald, Michal Januszewski, Viren Jain, Joergen Kornfeld

Posted 06 Jul 2018
bioRxiv DOI: 10.1101/364034 (published DOI: 10.1038/s41467-019-10836-3)

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 their diverse application possibilities. 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 inferred morphology embeddings ("Neuron2vec") of neuron reconstructions and trained CMNs to identify glia cells in a supervised classification paradigm which was 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.

Download data

  • Downloaded 1,261 times
  • Download rankings, all-time:
    • Site-wide: 5,911 out of 73,470
    • In neuroscience: 925 out of 13,211
  • Year to date:
    • Site-wide: 38,119 out of 73,470
  • Since beginning of last month:
    • Site-wide: 38,119 out of 73,470

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)