kephale / predict-unet-copick / 0.0.8

Generate Segmentation Masks using UNet Checkpoint

Generate segmentation masks using a trained UNet checkpoint on the Copick dataset.
Tags
imagingcryoetPythonnapari
Citation
Cellcanvas team.https://cellcanvas.org
Solution written by
Kyle Harrington
License of solution
MIT
Source Code

Arguments

--copick_config_path
Path to the Copick configuration file (default value: PARAMETER_VALUE)
--run_name
Name of the run in the Copick project for testing (default value: PARAMETER_VALUE)
--tomo_type
Tomogram type in the Copick project (default value: PARAMETER_VALUE)
--user_id
User ID for the Copick project (default value: PARAMETER_VALUE)
--session_id
Session ID for the Copick project (default value: PARAMETER_VALUE)
--voxel_spacing
Voxel spacing for the Copick project (default value: PARAMETER_VALUE)
--checkpoint_path
Path to the trained UNet checkpoint (default value: PARAMETER_VALUE)
--segmentation_name
Name of the output segmentation (default value: PARAMETER_VALUE)
--batch_size
Batch size for inference (default value: 1)
--output_probability_maps
Whether to output probability maps (default value: 0)

Usage instructions

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