English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

RoboEM: automated 3D flight tracing for synaptic-resolution connectomics

MPS-Authors

Schmidt,  Martin
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

Motta ,  Alessandro
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

Sievers,  Meike
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;
Faculty of Science, Radboud University, Nijmegen, the Netherlands.;

/persons/resource/persons39222

Helmstaedter,  Moritz
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Schmidt, M., Motta, A., Sievers, M., & Helmstaedter, M. (2024). RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nat Methods. doi: 10.1038/s41592-024-02226-5.


Cite as: https://hdl.handle.net/21.11116/0000-000F-16CF-0
Abstract
Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.