Predict Binary Segmentations Using Models

A solution that predicts binary segmentations for each label using models created by an optimization solution, and saves them separately.

Arguments

--copick_config_path

Path to the Copick configuration JSON file. (default value: PARAMETER_VALUE)

--session_id

Session ID for the segmentation. (default value: PARAMETER_VALUE)

--user_id

User ID for segmentation creation. (default value: PARAMETER_VALUE)

--voxel_spacing

Voxel spacing used to scale pick locations. (default value: PARAMETER_VALUE)

--run_name

Name of the Copick run to process. (default value: PARAMETER_VALUE)

--tomo_type

Type of tomogram to use, e.g., denoised. (default value: PARAMETER_VALUE)

--feature_names

Comma-separated list of feature names to use, e.g., cellcanvas01,cellcanvas02. (default value: PARAMETER_VALUE)

--model_dir

Directory containing the trained models. (default value: PARAMETER_VALUE)

--segmentation_name

Name of the output segmentation. (default value: PARAMETER_VALUE)

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