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Synaptic Partner Prediction from Point Annotations in Insect Brains

  • Julia BuhmannEmail author
  • Renate KrauseEmail author
  • Rodrigo Ceballos Lentini
  • Nils Eckstein
  • Matthew Cook
  • Srinivas Turaga
  • Jan Funke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

High-throughput electron microscopy allows recording of large stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identified as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple postsynaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more expensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3% fewer errors than the next best method (Code at: https://github.com/juliabuhmann/syntist).

Notes

Acknowledgements

This work was funded by the SNF grants P2EZP2_165241 and 205321L_160133.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Julia Buhmann
    • 1
    Email author
  • Renate Krause
    • 1
    • 2
    Email author
  • Rodrigo Ceballos Lentini
    • 1
  • Nils Eckstein
    • 1
  • Matthew Cook
    • 1
  • Srinivas Turaga
    • 2
  • Jan Funke
    • 2
    • 3
  1. 1.Institute of Neuroinformatics UZH/ETHZZurichSwitzerland
  2. 2.HHMI Janelia Research CampusAshburnUSA
  3. 3.Institut de Robotica i Informatica Industrial, UPCBarcelonaSpain

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