cellcoloc.refine_run_result_from_cellpose_cache

cellcoloc.refine_run_result_from_cellpose_cache(loaded_images, roi_labels_2d, run_result, colocalization_config, cell_model_config=None, marker_model_config=None, optional_region_model_config=None, cell_cellprob_threshold=None, cell_flow_threshold=None, marker_cellprob_threshold=None, marker_flow_threshold=None, optional_region_cellprob_threshold=None, optional_region_flow_threshold=None, optional_region_result=None)[source]

Recompute masks and tables from cached Cellpose outputs.

This avoids rerunning the neural network forward pass and only recomputes the mask generation stage from cached dP and cellprob arrays. Passing cell_model_config=None, marker_model_config=None, and/or optional_region_model_config=None leaves the respective channel unchanged and reuses the masks already stored in run_result.

Any z-crop defined in the supplied refinement configs is interpreted as one global analysis z range and applied consistently across all channels. When no refinement config specifies a z-crop, the function preserves the z-bounds stored in run_result. When the loaded images already represent a z-projection, additional z-cropping is ignored because the data have already been collapsed to a singleton-z analysis view.

Return type:: ColocalizationRunResult