Commit 9dcfa400 authored by Philipp Schubert's avatar Philipp Schubert
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updated docs

parent 0fb9b4fb
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# SyConn
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).
......@@ -60,7 +63,6 @@ Publications
}
```
\[2\] [Learning cellular morphology with neural networks](https://doi.org/10.1038/s41467-019-10836-3)
```
@Article{Schubert2019,
......
......@@ -72,37 +72,34 @@ finished the following analysis steps for an example cube of shape \[2180 2180 1
\[8/8] Matrix export 00h:00min:37s 0%
## More examples
[here](examples.md)
## Example scripts and API usage
Example scripts can be found [here](examples.md) and code examples [here](api.md).
## SyConn KNOSSOS viewer
The following packages have to be available for the system's python2 interpreter
(will differ from the conda environment):
## Package structure and data classes
The basic data structures and initialization procedures are explained in the following sections:
- numpy
* SyConn operates with a pre-defined [working directory and config files](config.md)
- lz4
* Super voxels (and cellular organelles) are stored in the SegmentationObject data class ([SO](segmentation_datasets.md)), which are
organized in [SegmentationDatasets](segmentation_datasets.md).
- requests
* SyConn principally supports different [backends](backend.md) for data storage, the current default is a simple shared filesystem
(such as lustre, Google Cloud Filestore or AWS Elastic File System).
In order to inspect the resulting data via the SyConnViewer KNOSSOS-plugin follow these steps:
* Agglomerated super voxels (SVs) are implemented as SuperSegmentationObjects ([SSO](super_segmentation_objects.md)). The collection
of super-SVs are usually defined in a region adjacency graph (RAG) which is used to initialize the SuperSegmentationDataset
([SSD](super_segmentation_datasets.md)).
- Wait until `start.py` finished. For restarting the server run `SyConn/scripts/kplugin/server.py --working_dir=<path>`
pointing to your working directory (`<path>`). The server address and port will be printed here.
* [Skeletons](skeletons.md) of (super) super voxels, usually computed from variants of the TEASAR algorithm (https://ieeexplore.ieee.org/document/883951).
- Download and run the nightly build of KNOSSOS (https://github.com/knossos-project/knossos/releases/tag/nightly)
* [Mesh](meshes.md) generation and representation of SOs
- In KNOSSOS -> File -> Choose Dataset -> browse to your working directory and open
`knossosdatasets/seg/mag1/knossos.conf` with enabled 'load_segmentation_overlay' (at the bottom of the dialog).
* Multi-view representation of SSOs (see docs for [glia](glia_removal.md) and [neuron](neuron_analysis.md) analysis and [article](https://www.nature.com/articles/s41467-019-10836-3) in Nature Communications)
- Then go to Scripting (top row) -> Run file -> browse to `SyConn/scripts/kplugin/syconn_knossos_viewer.py`, open it and enter
the port and address of the syconn server.
- After the SyConnViewer window has opened, the selection of segmentation fragments in the slice-viewports (exploration mode) or in the
list of cell IDs followed by pressing 'show neurite' will trigger the rendering of the corresponding cell reconstruction mesh in the 3D viewport.
The plugin will display additional information about the selected cell and a list of detected synapses (shown as tuples of cell IDs;
clicking the entry will trigger a jump to the synapse location) and their respective
properties. In case the window does not pop-up check Scripting->Interpreter for errors.
## Flowchart of SyConn
<img src="https://docs.google.com/drawings/d/e/2PACX-1vSY7p2boPxb9OICxNhSrHQlvuHTBRbSMeIOgQ4_NV6pflxc0FKJvPBtskYMAgJsX_OP-6CNmb08tLC5/pub?w=1920&amp;h=800">
## Analysis steps
......@@ -119,28 +116,31 @@ compartments (e.g. axons and spines) and to perform morphology based cell type c
* [Identification of synapses and extraction of a wiring diagram](contact_site_classification.md) (steps 4 and 8)
## Package structure and data classes
The basic data structures and initialization procedures are explained in the following sections:
* SyConn operates with a pre-defined [working directory and config files](config.md)
## SyConn KNOSSOS viewer
The following packages have to be available for the system's python2 interpreter
(will differ from the conda environment):
* Super voxels (and cellular organelles) are stored in the SegmentationObject data class ([SO](segmentation_datasets.md)), which are
organized in [SegmentationDatasets](segmentation_datasets.md).
- numpy
* SyConn principally supports different [backends](backend.md) for data storage, the current default is a simple shared filesystem
(such as lustre, Google Cloud Filestore or AWS Elastic File System).
- lz4
* Agglomerated super voxels (SVs) are implemented as SuperSegmentationObjects ([SSO](super_segmentation_objects.md)). The collection
of super-SVs are usually defined in a region adjacency graph (RAG) which is used to initialize the SuperSegmentationDataset
([SSD](super_segmentation_datasets.md)).
- requests
* [Skeletons](skeletons.md) of (super) super voxels, usually computed from variants of the TEASAR algorithm (https://ieeexplore.ieee.org/document/883951).
In order to inspect the resulting data via the SyConnViewer KNOSSOS-plugin follow these steps:
* [Mesh](meshes.md) generation and representation of SOs
- Wait until `start.py` finished. For restarting the server run `SyConn/scripts/kplugin/server.py --working_dir=<path>`
pointing to your working directory (`<path>`). The server address and port will be printed here.
* Multi-view representation of SSOs (see docs for [glia](glia_removal.md) and [neuron](neuron_analysis.md) analysis and [article](https://www.nature.com/articles/s41467-019-10836-3) in Nature Communications)
- Download and run the nightly build of KNOSSOS (https://github.com/knossos-project/knossos/releases/tag/nightly)
- In KNOSSOS -> File -> Choose Dataset -> browse to your working directory and open
`knossosdatasets/seg/mag1/knossos.conf` with enabled 'load_segmentation_overlay' (at the bottom of the dialog).
## Flowchart of SyConn
- Then go to Scripting (top row) -> Run file -> browse to `SyConn/scripts/kplugin/syconn_knossos_viewer.py`, open it and enter
the port and address of the syconn server.
<img src="https://docs.google.com/drawings/d/e/2PACX-1vSY7p2boPxb9OICxNhSrHQlvuHTBRbSMeIOgQ4_NV6pflxc0FKJvPBtskYMAgJsX_OP-6CNmb08tLC5/pub?w=1920&amp;h=1024">
- After the SyConnViewer window has opened, the selection of segmentation fragments in the slice-viewports (exploration mode) or in the
list of cell IDs followed by pressing 'show neurite' will trigger the rendering of the corresponding cell reconstruction mesh in the 3D viewport.
The plugin will display additional information about the selected cell and a list of detected synapses (shown as tuples of cell IDs;
clicking the entry will trigger a jump to the synapse location) and their respective
properties. In case the window does not pop-up check Scripting->Interpreter for errors.
\ No newline at end of file
......@@ -2,7 +2,7 @@
This section introduces minimal examples for parts of SyConn. The corresponding python scripts
can be found in `SyConn/examples/`.
## Semantic segmentation
## Semantic segmentation if spines
* python script: `SyConn/examples/semseg_spine.py`
* requires model folder in working directory
......@@ -29,3 +29,9 @@ After completion you will find a second file which contains the cell mesh colore
model's output.
The following two images show an example of the original data (content of `(\d+).k.zip`) and the prediction (content of `(\d+)_spines.k.zip`; red: spine head, black: dendritic shaft; grey: spine neck; turquoise: soma/axon)
## Semantic segmentation of (other) cell compartments
* python script: `SyConn/examples/semseg_axon.py`
* requires model folder in working directory
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