SuperSegmentation datasets

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.

The first initializing of an SSD usually happens after glia removal.

Prerequisites

• Knossos- and SegmentationDataset of the supervoxel segmentation

• Initial RAG/SV-agglomeration

Initialization

In order to create a SuperSegmentationDataset from scratch one has to provide the agglomerated supervoxel (SSV) defined as a dict (coming soon!; AGG_SOURCE; keys: SSV IDs and values: list of SVs) or stored as a KNOSSOS mergelist (text file; variable holding the path string: AGG_SOURCE) and pass it to the constructor (kwarg: ‘sv_mapping’). The version kwarg is used to distinguish between different SSV datasets, e.g. if one is interested in separating the initial RAG into neuron and glia segmentation one could use version='glia' and version='neuron'. By default, the version is incremented by one starting at 0 for same ssd_type’s.

ssd = ss.SuperSegmentationDataset(working_dir=WORKING_DIR,
                                  version=VERSION, ssd_type="ssv",
                                  sv_mapping=AGG_SOURCE)
ssd.save_dataset_shallow()
ssd.save_dataset_deep()
# alternatively for small datasets: ssd.save_dataset_deep(nb_cpus=20)

A summary script for the initial SSD generation, called create_ssd.py, can be found at SyConn/scripts/. It combines the above procedures, the mapping of cellular organelles and saves a SV-graph for every SSV.