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Synaptic Cleft Segmentation in Non-isotropic Volume Electron Microscopy of the Complete Drosophila Brain

  • Larissa Heinrich
  • Jan Funke
  • Constantin Pape
  • Juan Nunez-Iglesias
  • Stephan Saalfeld
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections.

Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation.

We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art.

We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Larissa Heinrich
    • 1
  • Jan Funke
    • 1
    • 2
  • Constantin Pape
    • 1
    • 3
  • Juan Nunez-Iglesias
    • 1
  • Stephan Saalfeld
    • 1
  1. 1.HHMI Janelia Research CampusAshburnUSA
  2. 2.Institut de Robòtica i Informàtica IndustrialBarcelonaSpain
  3. 3.University of HeidelbergHeidelbergGermany

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