Cellfinder includes a pretrained network for cell candidate classification. This will likely need to be retrained for different applications. Rather than generate training data blindly, the aim is to reduce the amount of hands-on time by only generating training data where cellfinder classified a cell candidate incorrectly.
To generate training data, you will need:
The cellfinder output file,
cells.xml can also work).
The raw data used initially for cellfinder
To generate training data for a single brain, use
cellfinder_curate signal_images background_images cell_classification.xml
--output Output directory for curation results. If this is not given, then the directory containing
cell_classification.xml will be used.
--symbol Marker symbol (Default:
--marker-size Marker size(Default:
--opacity Marker opacity (Default:
A napari window will then open, showing two tabs on the left hand side:
Image Selecting this allows you to change the contrast limits, to better visualise cells
Cell candidates This shows the cell candidates than be curated. Cell
candidates previously classified as cells are shown in yellow, and artifacts
By selecting the
Cell candidates tab and then the cell selecting tool (arrow at the top), cell candidates can be selected (either individually, or many by dragging the cursor). There are then four keyboard commands:
C Confirm the classification result, and add this to the training set
T Toggle the classification result (i.e. change the classification),
and add this to the training set.
Alt+Q Save the results to an xml file
Alt+E Finish curating the training dataset. This will carry out three operations:
Extract cubes around these points, into two directories (
Generate a yaml file pointing to these files for use with
cellfinder_train (see below)
Close the viewer
yaml file has been generated, you can proceed to training. However, it is likely useful to generate
yaml files from additional datasets.
You can then use these yaml files for training
cellfinder_train -y yaml_1.yml yaml_2.yml -o /path/to/output/directory/
--yaml The path to the yaml files defining training data
--output Output directory for the trained model (or model weights)
--continue-training Continue training from an existing trained model. If no model or model weights are specified, this will continue from the included model.
--trained-model Path to a trained model to continue training
--model-weights Path to existing model weights to continue training
--network-depth Resnet depth (based on He et al. (2015)). Choose from
(18, 34, 50, 101 or 152). In theory, a deeper network should classify better,
at the expense of a larger model, and longer training time. Default: 50
--batch-size Batch size for training (how many cell candidates to process at once). Default: 16
--epochs How many times to use each sample for training. Default: 1000
--test-fraction What fraction of data to keep for validation. Default: 0.1
--learning-rate Learning rate for training the model
--no-augment Do not use data augmentation
--save-weights Only store the model weights, and not the full model. Useful to save storage space.
--no-save-checkpoints Do not save the model after each training epoch. Useful to save storage space, if you are happy to wait for the chosen number of epochs to complete. Each model file can be large, and if you don't have much training data, they can be generated quickly.
--tensorboard Log to
tensorboard --logdir outputdirectory/tensorboard to view.
--save-progress Save training progress to a .csv file (
cellfinder_train options can be found by running: