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Synaptic Partner Prediction from Point Annotations in Insect Brains


Buhmann, Julia; Krause, Renate; Lentini, Rodrigo Ceballos; Eckstein, Nils; Cook, Matthew; Turaga, Srinivas; Funke, Jan (2018). Synaptic Partner Prediction from Point Annotations in Insect Brains. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Granada, 16 September 2018 - 20 September 2018, 309-316.

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).

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).

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:20 September 2018
Deposited On:12 Mar 2019 12:13
Last Modified:27 Mar 2019 15:21
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:11071
Number of Pages:8
ISSN:0302-9743
ISBN:978-3-030-00933-5
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-030-00934-2_35

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