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[![Documentation Status](https://readthedocs.org/projects/syconn/badge/?version=latest)](https://syconn.readthedocs.io/en/latest/?badge=latest)

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# SyConn
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Refactored version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data. For the first version
 see below or checkout the branch [dorkenwald2017nm](https://github.com/StructuralNeurobiologyLab/SyConn/tree/dorkenwald2017nm).
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Current features:
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- introduction of supervoxel and agglomerated supervoxel classes
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- added support for (sub-) cellular compartment (spines, axon/dendrite/soma) and cell type classification with skeleton- [\[1\]](https://www.nature.com/articles/nmeth.4206) and multiview-based [\[2\]](https://www.nature.com/articles/s41467-019-10836-3) approaches
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- cell organelle prediction, extraction and mesh generation
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- glia identification and separation [\[2\]](https://www.nature.com/articles/s41467-019-10836-3)
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- generation of connectivity matrix

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If you use parts of this code base in your academic projects, please cite the corresponding publication.

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Documentation
-------------
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The documentation for the refactored version is still work-in-progress and can be found [here](docs/doc.md). Alternatively see the latest [readthedocs build](https://syconn.readthedocs.io/en/latest/).

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For SyConn v1, please have a look at the old [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/). We also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).

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The Team
--------
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The Synaptic connectivity inference toolkit is developed at the Max-Planck-Institute of Neurobiology in Martinsried by
 Philipp Schubert, Maria Kawula, Carl Constantin v. Wedemeyer, Atul Mohite, Gaurav Kumar and Joergen Kornfeld.


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Acknowledgements
----------------
We are especially grateful for the support by Winfried Denk who enabled this work in his department. We also want to thank Christian Guggenberger
and his group at the MPCDF for cluster support and deepmind for providing egl extension code to handle multi-gpu rendering on the same machine.
The original code snippet (under the Apache License 2.0) used for our project can be found
[here](https://github.com/deepmind/dm_control/blob/30069ac11b60ee71acbd9159547d0bc334d63281/dm_control/_render/pyopengl/egl_ext.py).
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References
----------
\[1\] [Automated synaptic connectivity inference for volume electron microscopy](https://www.nature.com/articles/nmeth.4206)
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```
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 @ARTICLE{SyConn2017,
   title     = "Automated synaptic connectivity inference for volume electron
                microscopy",
   author    = "Dorkenwald, Sven and Schubert, Philipp J and Killinger, Marius F
                and Urban, Gregor and Mikula, Shawn and Svara, Fabian and
                Kornfeld, Joergen",
   abstract  = "SyConn is a computational framework that infers the synaptic
                wiring of neurons in volume electron microscopy data sets with
                machine learning. It has been applied to zebra finch, mouse and
                zebrafish neuronal tissue samples.",
   journal   = "Nat. Methods",
   publisher = "Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.",
   year      = 2017,
   month     = Feb,
   day       = 27,
   url       = http://dx.doi.org/10.1038/nmeth.4206
 }
  ```
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\[2\] [Learning cellular morphology with neural networks](https://doi.org/10.1038/s41467-019-10836-3)
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  ```
  @Article{Schubert2019,
author={Schubert, Philipp J.
and Dorkenwald, Sven
and Januszewski, Michal
and Jain, Viren
and Kornfeld, Joergen},
title={Learning cellular morphology with neural networks},
journal={Nature Communications},
year={2019},
volume={10},
number={1},
pages={2736},
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abstract={Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but
can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated
neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological
analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications.
Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically
reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings
(Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm,
which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify
subcellular compartments and the cell types of neuron reconstructions.},
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issn={2041-1723},
doi={10.1038/s41467-019-10836-3},
url={https://doi.org/10.1038/s41467-019-10836-3}
}
  ```