Integration of new cell-organelle-types

The following lists the steps that need to be taken to integrate new types of cell organelles into the prediction and segmentation pipeline:

1. Choose a short but clear name for the cell-organelle-type which is supposed to be integrated, which in the following is used as an identifier for the cell-organelle-type.
   Examples for implemented cell-organelle-types are: ‘mi’ for mitochondria, ‘er’ for the endoplasmic reticulum and ‘golgi’ for the golgi apparatus.
   In the following, the exemplary cell-organelle-name ‘co’ is used. It will have to be replaced with the chosen name.

2. In syconn/exec/exec_dense_prediction.py add a function predict_co(), analogue to the other functions for prediction of cell organelles (e.g. predict_er or predict_golgi).
   Within this function make sure to replace the cell-organelle-name in the parameter values passed to predict_dense_to_kd() accordingly; in particular global_params.config.mpath_co and target_names=['co'].
   The simplest case would be to choose n_channel=2, one for the background volume and one for the corresponding cell-organelle-volume. If different channels are supposed to be predicted in this step, these parameters will need to be adapted accordingly.

3. In syconn/handler/config.py in class DynConfig add property-method kd_co_path, which returns self.entries['paths']['kd_co'].

4. Also in syconn/handler/config.py in class DynConfig add property-method mpath_co, which returns self.model_dir + '/co/model.pts'.
   Correspondingly in the model directory add a directory co, in which the corresponding model for the prediction is stored.

5. Also in syconn/handler/config.py give the function generate_default_conf() an additional parameter kd_co with default value None.
   Within the function, set kd_co to working_dir + 'knossosdatasets/co/' if it was passed as None.
   Within the function add entries['paths']['kd_er'] = kd_er to write the given path of the knossos_dataset into the config-file.

6. In syconn/handler/config.yml make the following changes:

   • To the list process_cell_organelles add the element co.

   • Under paths: with an indentation add the line kd_co: (include the :).

   • Under versions: with an indentation add the line co: version_no, where version_no typically is 0.

   • Under cell_objects:, under min_obj_vx:, with an indentation add the line co: min_vx_no, where min_vx_no is an integer set in a sensible manner. It specifies the minimum number of voxels an object needs to have, to be identified as an example of the corresponding cell-organelle-type.
     E.g. it is 500 for a large structure like the mitochondrion, but for a small structure like the synaptic vesicles, it is 1.

   • Under cell_objects:, under probathresholds:, with an indentation add the line co: thresh, where thresh is the probability-threshold for the generation of a segmentation dataset from a probability dataset and needs to be set to a number. Without any extra information, a reasonable choice is thresh=0.5.

   • Under cell_objects:, under min_seed_vx:, with an indentation add the line co: min_seed, with min_seed the seed for the watershed-algorithm (see description in the file).

   • Under cell_objects:, under extract_morph_op:, with an indentation add the line co: morph_ops, where morph_ops is a list of possible morphological operations. See explanation in file.

   • Under meshes:, under downsampling:, with an indentation add the line co: mesh_down, where mesh_down is a list of length 3, describing the downsampling.

All the steps taken above are implemented analogously for er and golgi already, which could be used as examples and base for further organelles to be integrated.