cellcanvas / generate-pixel-embedding / 0.1.6

Predict Tomogram Embeddings with SwinUNETR using Copick API and generate the tensorboard files

Apply a SwinUNETR model to a tomogram fetched using the Copick API to produce embeddings, and save them in a Zarr.
Tags
imagingcryoetPython
Solution written by
Kyle Harrington
License of solution
MIT
Source Code

Arguments

--copick_config_path
Path to the Copick configuration JSON file. (default value: PARAMETER_VALUE)
--run_name
Name of the Copick run to process. (default value: PARAMETER_VALUE)
--voxel_spacing
Voxel spacing to be used. (default value: PARAMETER_VALUE)
--tomo_type
Type of tomogram to process. (default value: PARAMETER_VALUE)
--checkpointpath
Path to the checkpoint file of the trained SwinUNETR model (default value: PARAMETER_VALUE)
--embedding_name
Name of the embedding to use as the feature name in Copick (default value: PARAMETER_VALUE)

Usage instructions

Please follow this link for details on how to install and run this solution.