Commit 2dd8b748 authored by Philipp Schubert's avatar Philipp Schubert
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README update

parent a0e6a257
[![Documentation Status](https://readthedocs.org/projects/syconn/badge/?version=latest)](https://syconn.readthedocs.io/en/latest/?badge=latest)
# SyConn
Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data.
For v0.1 see the SyConn branch [dorkenwald2017nm](https://github.com/StructuralNeurobiologyLab/SyConn/tree/dorkenwald2017nm).
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).
v0.2 currently features:
Current features:
- introduction of supervoxel and agglomerated supervoxel classes
- added support for (sub-) cellular compartment (spines, axon/dendrite/soma) and cell type classification with [skeleton](https://www.nature.com/articles/nmeth.4206)- and [multiview-based](https://www.biorxiv.org/content/early/2018/07/06/364034) approaches
- 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.biorxiv.org/content/early/2018/07/06/364034) approaches
- cell organelle prediction, extraction and mesh generation
- [glia identification and splitting](https://www.biorxiv.org/content/early/2018/07/06/364034)
- glia identification and supervoxel graph splitting [\[2\]](https://www.biorxiv.org/content/early/2018/07/06/364034)
- generation of connectivity matrix
## System requirements & installation
* Python 3.5
* The whole pipeline was designed and tested on Linux systems (CentOS, Arch)
* SyConn is primarily based on the packages [elektronn](http://elektronn.org) and [knossos-utils](https://github.com/knossos-project/knossos_utils)
* We use [KNOSSOS](https://knossostool.org/)
for visualization and annotation of 3D EM data sets.
* [VIGRA](https://ukoethe.github.io/vigra/), e.g. ``conda install -c ukoethe vigra``
* osmesa, e.g.: ``conda install -c menpo osmesa``
You can install SyConn using ``git`` and ``pip``:
git clone https://github.com/SyConn
cd SyConn
pip install -r requirements.txt
pip install .
## Tutorials & documentation
For the SyConn documentation see [here](docs/doc.md) or check out the latest readthedocs build [here](https://syconn.readthedocs.io/en/latest/). Alternatively you can build the API documentation locally by running `make html` in the `docs` folder.
Documentation
--------------
To get started, please have a look at our [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/), but be aware that it is currently outdated and applies only to SyConn v1. We also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).
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/).
SyConn v1 Publication
---------------------
The first version of SyConn (see branch [dorkenwald2017nm](https://github.com/StructuralNeurobiologyLab/SyConn/tree/dorkenwald2017nm)) was published in [Nature Methods](http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4206.html) on February 27th 2017. If you use parts of this code base in your academic projects, please cite the corresponding publication. <br />
```
@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
}
```
# The Team
The Synaptic connectivity inference toolkit is developed at Max-Planck-Institute of Neurobiology, Munich.
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