Cell Labeling Scripts

This Python module contains utility methods related to:

• generating labeled masks

• converting annotations

• processing segmentation outputs

• training a model (via CLI argument)

This program works with the cell detection process. Currently, the program works with one animal and one task at a time. All models are stored in /net/birdstore/Active_Atlas_Data/cell_segmentation/models/ The models are named by step: models_step_X_threshold_2000.pkl where ‘X’ is the step number. The program can be run with the following commands:

• python srs/labeling/scripts/create_labels.py –animal DKXX –task segment

• python srs/labeling/scripts/create_labels.py –animal DKXX –task detect

• python srs/labeling/scripts/create_labels.py –animal DKXX –task extract

• python srs/labeling/scripts/create_labels.py –animal DKXX –task train

• python srs/labeling/scripts/create_labels.py –animal DKXX –task prune

Explanation for the tasks:

• detect - This is the 1st task to run and will create the cell_labels/detection_XXX.csv files. This task will run the cell detection model on the images and create the detections.

• extract - This task will extract the predictions from the detection files and create the cell_labels/all_predictions.csv file.

• train - This task creates the detection_XXX.csv files created above and trains the model. The features are taken from the detection_XXX.csv files. The model is saved in the cell_segmentation/models dir. This new model can then be used to rerun the detection process. Repeat as necessary.

• fix - This is only needed when the images have the extra tissue and skull present. You will need to create the rotated and aligned masks for the images.

Training workflow:: - The supervised models used require manual steps for processing (see )

• Detect cells on available brains.

• Some of the brains have too many points to easily display in Neuroglancer as annotation layers

  and will crash the browser. We create them as a precomputed data format, which can be loaded in Neuroglancer as segmentation layer [this is included when running task:detect]

@end of ‘detect’ task, artifacts: [on birdstore]: preps/DIFF_{uuid} #not removed www/histogram/DIFF_{uuid} #not removed www/neuroglancer/DIFF_{uuid} #not removed www/neuroglancer_data/progress/DIFF_{uuid} #removed

[on compute server] #all removed /scratch/pipeline_tmp/{animal}/cell_candidates_{uuid} /scratch/pipeline_tmp/{animal}/cell_features_{uuid} /scratch/pipeline_tmp/{animal}/DIFF_{uuid} /scratch/pipeline_tmp/{animal}/DIFF_candidates_{uuid}_py

class labeling.scripts.create_labels.RangeOrIntAction(option_strings, dest, nargs=None, const=None, default=None, type=None, choices=None, required=False, help=None, metavar=None)

Bases: Action

Detecting Cells

To run cell detection/segmentation using a trained model, use the –task detect argument:

python -m labeling.scripts.create_labels --task detect --animal {ANIMAL_ID} --model {neuron_model_type}

This mode:

• Loads the trained model from the centralized model directory

• Loads average cell image on 2 channels (virus and dye)

• Calculates features for cell candidates: energy coorelation and Hu moments

• Applies the model to score the cell candidates

• Saves detection results (e.g. detections_{section}.csv) to: /net/birdstore/Active_Atlas_Data/cell_segmentation/data_root/pipeline_data/{ANIMAL_ID}/preps/cell_labels/

Optional: Ensure the image stacks (tif or OME-Zarr) are available in preps folder, and that the specified model exists if argument –model is used.

Training the Model

To train the cell labeling model, run the script with the –train argument:

python -m labeling.scripts.create_labels --task train --animal {ANIMAL_ID} --model {neuron_model_type} --step 1

This mode:

• Reads the ‘ground truth’ neuron coordinates from cell_labels{step} directory

• Trains specific model type using supplied putative identifications (positive, negative)

• Saves model to centralized location (with metrics) to /net/birdstore/Active_Atlas_Data/cell_segmentation/models/