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

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14 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of Medical Image Computing and Computer Assisted Intervention - MICCAI 2018
EditorsAlejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-Lopez, Gabor Fichtinger
Place of PublicationSwitzerland
Number of pages9
ISBN (Electronic)9783030009342
ISBN (Print)9783030009335
Publication statusPublished - 20 Sep 2018
Externally publishedYes
EventMedical Image Computing and Computer-Assisted Intervention 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018
Conference number: 21st

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11071 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMedical Image Computing and Computer-Assisted Intervention 2018
Abbreviated titleMICCAI 2018
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