Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Learning cellular morphology with neural networks

Philipp J Schubert, Sven Dorkenwald, Michał Januszewski, Viren Jain, Joergen Kornfeld
doi: https://doi.org/10.1101/364034
Philipp J Schubert
Max Planck Institute of Neurobiology, Electrons - Photons - Neurons, Planegg-Martinsried, 82152, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sven Dorkenwald
Max Planck Institute of Neurobiology, Electrons - Photons - Neurons, Planegg-Martinsried, 82152, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michał Januszewski
Google AI, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Viren Jain
Google AI, Mountain View, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joergen Kornfeld
Max Planck Institute of Neurobiology, Electrons - Photons - Neurons, Planegg-Martinsried, 82152, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

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 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted July 06, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Learning cellular morphology with neural networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Learning cellular morphology with neural networks
Philipp J Schubert, Sven Dorkenwald, Michał Januszewski, Viren Jain, Joergen Kornfeld
bioRxiv 364034; doi: https://doi.org/10.1101/364034
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Learning cellular morphology with neural networks
Philipp J Schubert, Sven Dorkenwald, Michał Januszewski, Viren Jain, Joergen Kornfeld
bioRxiv 364034; doi: https://doi.org/10.1101/364034

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (1647)
  • Biochemistry (2739)
  • Bioengineering (1907)
  • Bioinformatics (10243)
  • Biophysics (4183)
  • Cancer Biology (3218)
  • Cell Biology (4539)
  • Clinical Trials (135)
  • Developmental Biology (2840)
  • Ecology (4461)
  • Epidemiology (2041)
  • Evolutionary Biology (7231)
  • Genetics (5477)
  • Genomics (6813)
  • Immunology (2390)
  • Microbiology (7485)
  • Molecular Biology (2993)
  • Neuroscience (18584)
  • Paleontology (136)
  • Pathology (472)
  • Pharmacology and Toxicology (780)
  • Physiology (1150)
  • Plant Biology (2706)
  • Scientific Communication and Education (680)
  • Synthetic Biology (888)
  • Systems Biology (2846)
  • Zoology (468)