Contact sites are the basis for synaptic classification. Therefore, contact sites need to be combined with the synapse `SegmentationObjects` and then classified as synaptic or not-synaptic using an Random Forest Classifier (RFC).
The code is in `syconn.extraction.cs_processing_steps`, `syconn.proc.sd_proc` and `syconn.proc.ssd_proc`.
## Synapse type [TODO: check where this information is actually needed!]
Information of the [synapse type](synapse_type.md) can be inferred from a trained CNN model and be used in the wiring diagram in a later state.
## Overlap mapping
Synapse `SegmentationObjects` are mapped to contact sites by volume overlap the same way `SegmentationObjects` are mapped to supervoxels. First, the aggreagted contact sites (see `contact_site_extraction`) need to be exported to a `knossosdataset`:
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@@ -41,6 +44,7 @@ creates the ground truth for the RFC and also trains and stores the classifier.
Probability maps and segmentations are stored in `ChunkDatasets` (see `chunky.py` in `knossos_utils`)
and are transformed to `SegmentationDatasets` (see `segmentationdataset` in `syconn.reps`) in multiple steps.
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@@ -20,11 +20,18 @@ specific prediction within the `ChunkDataset` and the `hdf5names` in the file th
The wrappers sequentially call specific functions from `object_extraction_steps.py`. Parallelism is only
possible within these steps. `from_ids_to_objects` starts at step 4.
1.**Connected components** within each chunk are created for each chunk by applying a Gaussian smoothing (optional) and threshold first (`gauss_threshold_connected_components(...)`).
1.**Connected components** within each chunk (chunk-wise segmentation) are created for
by applying a Gaussian smoothing (optional) and threshold first (see method `object_segmentation(...)`).
Note that the default procedure with smoothing and a subsequent thresholding can be replaced by
passing a custom-method to the method via the kwargs `transform_func=None, func_kwargs=None`. The provided method has to
obey the function signature of `_gauss_threshold_connected_components_thread`.
2.`make_unique_labels` reassignes globally **unique labels** to all segments
3.`make_stitch_list` collects information of which segments in different chunks are in fact the same and `make_merge_list` resolves this to a global **mergelist** that is then applied by `apply_merge_list`.
4.`extract_voxels` writes the voxels of each object to a **temporary voxel storage** (similar to the voxel store of a `SegmentationDataset`) and guarantees no write conflicts.
5. In `combine_voxels` each worker then reads the voxels belonging to each object from the temporary voxel storage and writes them to their final location, essentially **creating a `SegmentationDataset`**.
3.`make_stitch_list` collects information of which segments in different
chunks are in fact the same and `make_merge_list` resolves this to a global **mergelist** that is then applied by `apply_merge_list`.
4.`extract_voxels` writes the voxels of each object to a **temporary voxel storage** (
similar to the voxel store of a `SegmentationDataset`) and guarantees no write conflicts.
5. In `combine_voxels` each worker then reads the voxels belonging to each object from the
temporary voxel storage and writes them to their final location, essentially **creating a `SegmentationDataset`**.
Steps 4 and 5 are necessary to prevent two workers to write to the same `VoxelDict` (hence, to avoid having locks) . This would happen because an object extends
over multiple chunks or because the ids of two different objects are assigned to the same `VoxelDict`. it also allows to balancing the
`SuperSegmentationDatasets` (SSD) and `SuperSegmentationObjects` (SSO; see corresponding section) are implemented in `super_segmentation_object.py` and `super_segmentation_object` (`syconn.reps`).
It is accompanied by helper functions in `super_segmentation_helper.py` for basic functionality such as loading and storing and
`ssd_proc.py` and `ssd_proc.assembly` (`syconn.proc`) which contain processing methods.
`SuperSegmentationDatasets` (SSD) and `SuperSegmentationObjects` (SSO; see corresponding section)
are implemented in `super_segmentation_object.py` and `super_segmentation_object` (`syconn.reps`).
It is accompanied by helper functions in `super_segmentation_helper.py` for basic functionality such as
loading and storing and `ssd_proc.py` and `ssd_proc.assembly` (`syconn.proc`) which contain processing methods.
Typically, initializing the SSD happens after glia removal.
The first initializing of an SSD usually happens after glia removal.
Please check the corresponding documentation to learn more about that.
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@@ -12,11 +13,9 @@ Please check the corresponding documentation to learn more about that.
In order to create a SuperSegmentationDataset from scratch one has to provide
the agglomerated super voxel (SSV) defined as a dict (coming soon!; agglomeration_source; keys: SSV IDs and values: list of SVs) or stored as a
KNOSSOS mergelist (text file; variable holding the path string: agglomeration_source) and parse it
KNOSSOS mergelist (text file; variable holding the path string: agglomeration_source) and pass it
