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# SyConn v2
Synaptic connectivity inference toolkit developed at Max-Planck-Institute of Neurobiology, Munich <br />
Authors: Philipp Schubert, Sven Dorkenwald, Joergen Kornfeld <br />
Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data. For the first version
see 'SyConn' below. Current features:
Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data.
For the first version see branch 'dorkenwald2017nm'.
Version 2 currently 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
- cell organelle prediction, extraction and mesh generation
- glia identification and splitting
- [glia identification and splitting](https://www.biorxiv.org/content/early/2018/07/06/364034)
- generation of connectivity matrix
Documentation
--------------
_in progress_
# SyConn
Synaptic connectivity inference toolkit developed at the Max-Planck-Institute for Medical Research, Heidelberg and
Max-Planck-Institute of Neurobiology, Munich <br />
Authors: Sven Dorkenwald, Philipp Schubert, Joergen Kornfeld <br />
Documentation
--------------
To get started, please have a look at our [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/), with information on how to run our [example](https://github.com/StructuralNeurobiologyLab/SyConn/blob/master/examples/full_run_example.py). We also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).
Publication
-----------
The first version of SyConn (see branch '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
}
```
## System Requirements & Installation
* Python 3.5
* The whole pipeline was designed and tested on Linux systems (CentOS, Arch)
* SyConn is based on the packages `elektronn <http://elektronn.org>`_, `knossos-utils <https://github.com/knossos-project/knossos_utils>`_
* `KNOSSOS <http://knossostool.org/>`_ is used 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 .
## Documentation
For documentation see [here](docs/doc.md)
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Examples
********
OUTDATED!
This page gives a brief example for a full run of then main components of SyConn.
The corresponding code is available in ``/examples/full_run_example.py`` and
the example data with all pre-trained models can be found in ``SyConnDenseCube(.zip)`` (go to our `Website <http://structuralneurobiologylab.github.io/SyConn/>`_ for Download).
......
......@@ -5,13 +5,11 @@ Getting Started
System Requirements & Installation
==================================
* Python 2.7
* Python 3.5
* The whole pipeline was designed and tested on Linux systems (CentOS, Arch)
* SyConn is based on the packages `ELEKTRONN <http://elektronn.org>`_, `Knossos Python-Tools <https://github.com/knossos-project/knossos_python_tools>`_
* SyConn is based on the packages `elektronn <http://elektronn.org>`_, `knossos-utils <https://github.com/knossos-project/knossos_utils>`_
* `KNOSSOS <http://knossostool.org/>`_ is used for visualization and annotation of 3D EM data sets.
For installation instructions see :ref:`installation <installation>`. We recommend using `Anaconda <https://www.continuum.io/downloads>`_ as
python distribution.
An code example can be found in section :ref:`examples <examples>`
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<li><a class="reference internal" href="#data-set">Data Set</a></li>
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<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline"></a></h1>
<p>This page gives a brief example for a full run of then main components of SyConn.
The corresponding code is available in <code class="docutils literal notranslate"><span class="pre">/examples/full_run_example.py</span></code> and
the example data with all pre-trained models can be found in <code class="docutils literal notranslate"><span class="pre">SyConnDenseCube(.zip)</span></code> (go to our <a class="reference external" href="http://structuralneurobiologylab.github.io/SyConn/">Website</a> for Download).</p>
<div class="section" id="running-the-example">
<h2>Running the example<a class="headerlink" href="#running-the-example" title="Permalink to this headline"></a></h2>
<p>Given that the example zipfile was extracted, running the analysis would just be:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python2</span> <span class="n">SyConn</span><span class="o">/</span><span class="n">examples</span><span class="o">/</span><span class="n">full_run_example</span><span class="o">.</span><span class="n">py</span> <span class="o">&lt;/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">example_folder</span><span class="o">&gt;</span> <span class="p">[</span><span class="o">--</span><span class="n">gpus</span> <span class="p">{</span><span class="kc">None</span> <span class="o">|</span> <span class="o">&lt;</span><span class="nb">int</span><span class="o">&gt;</span> <span class="o">&lt;</span><span class="nb">int</span><span class="o">&gt;</span> <span class="o">...</span><span class="p">},</span> <span class="o">--</span><span class="n">CNNsize</span> <span class="p">{</span><span class="mi">1</span> <span class="o">|</span> <span class="o">&lt;</span><span class="nb">int</span><span class="o">&gt;</span><span class="p">},</span> <span class="o">--</span><span class="n">qsub_pe</span> <span class="p">{</span><span class="kc">None</span> <span class="o">|</span> <span class="o">&lt;</span><span class="nb">str</span><span class="o">&gt;</span><span class="p">},</span> <span class="o">--</span><span class="n">qsub_queue</span> <span class="p">{</span><span class="kc">None</span> <span class="o">|</span> <span class="o">&lt;</span><span class="nb">str</span><span class="o">&gt;</span><span class="p">}]</span>
</pre></div>
</div>
<p>By default the CNN prediction uses only CPUs (None option; up to as many cores as specified in the ~/.theanorc).
GPUs can be made available by adding their ids (specified in nvidia-smi). The CNNsize argument allows the adjustment
of the memory used on each GPU, where 4 is the highest possible value (more VRAM used) and 0 the smallest value.
Defining either a qsub queue or qsub parallel environment lets the non-CNN part of the pipeline making use of a qsub
cluster.</p>
</div>
<div class="section" id="data-set">
<h2>Data Set<a class="headerlink" href="#data-set" title="Permalink to this headline"></a></h2>
<p>This data set is a subset of the zebra finch area X dataset j0126 acquired by
<a class="reference external" href="http://www.neuro.mpg.de/mitarbeiter/43611/3242756">Jörgen Kornfeld</a>.</p>
<div class="figure align-center" id="id2">
<a class="reference internal image-reference" href="_images/raw_cube_tracings.png"><img alt="_images/raw_cube_tracings.png" src="_images/raw_cube_tracings.png" style="width: 1365.6px; height: 537.0px;" /></a>
<p class="caption"><span class="caption-text">Left: Raw data cube; Right: Human cell tracings</span></p>
</div>
<p>The above figure shows cell tracings in 3D EM data cube, which is the
required data to perform automated structural analysis using SyConn.
The following section is a walk-through of all major steps.</p>
</div>
<div class="section" id="applying-syconn-step-by-step">
<h2>Applying SyConn – step by step<a class="headerlink" href="#applying-syconn-step-by-step" title="Permalink to this headline"></a></h2>
<p>At first, membrane and intracellular space is predicted as barrier (black) and cell
components, such as mitochondria (blue), vesicle clouds (green) and synaptic junctions (red)
are inferred using a CNN with a 3D perceptive field of view (image below). Additionally, another
3D CNN predicts symmetric and asymmetric synaptic junction regions which get combined with
the predicted synaptic junctions later on.</p>
<div class="figure align-center" id="id3">
<a class="reference internal image-reference" href="_images/raw_img_overlay_153.png"><img alt="_images/raw_img_overlay_153.png" src="_images/raw_img_overlay_153.png" style="width: 724.8px; height: 336.0px;" /></a>
<p class="caption"><span class="caption-text">Left: Slice of the raw cube; Right: Overlayed CNN probability maps</span></p>
</div>
<p>Thresholding and connected components analysis then reveal volumetric objects for each
class. Based on the cell tracings and the barrier prediction a cell hull sampling
is performed which enables the mapping of these objects to individual cells (embedding
tracings in biological environment). Furthermore, contact sites of adjacent
cell pairs can be distinguished from synapses combining properties of corresponding
contact site and, if existent, synaptic junction.</p>
<div class="figure align-center">
<a class="reference internal image-reference" href="_images/mapped_hull_synapse_new.png"><img alt="_images/mapped_hull_synapse_new.png" src="_images/mapped_hull_synapse_new.png" style="width: 676.8000000000001px; height: 463.20000000000005px;" /></a>
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<p>With these priors sub-cellular compartments can be inferred (axon/dendrite/soma and spines in dendrites)
to further assign types (EA, MSN, GP, INT) to every cell and create a wiring
diagram combining knowledge about pre and post synapses (sub-cellular compartments),
area of synaptic junctions and cell functions.</p>
<div class="figure align-center">
<a class="reference internal image-reference" href="_images/connectivity_labeled.png"><img alt="_images/connectivity_labeled.png" src="_images/connectivity_labeled.png" style="width: 480.0px; height: 375.70000000000005px;" /></a>
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<h2>System Requirements &amp; Installation<a class="headerlink" href="#system-requirements-installation" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li>Python 2.7</li>
<li>The whole pipeline was designed and tested on Linux systems (CentOS, Arch)</li>
<li>SyConn is based on the packages <a class="reference external" href="http://elektronn.org">ELEKTRONN</a>, <a class="reference external" href="https://github.com/knossos-project/knossos_python_tools">Knossos Python-Tools</a></li>
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<p>For installation instructions see <a class="reference internal" href="installation.html#installation"><span class="std std-ref">installation</span></a>. We recommend using <a class="reference external" href="https://www.continuum.io/downloads">Anaconda</a> as
python distribution.</p>
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<h1>All modules for which code is available</h1>
<ul><li><a href="syconn/config/parser.html">syconn.config.parser</a></li>
<li><a href="syconn/handler/basics.html">syconn.handler.basics</a></li>
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