1. Generate Segmentation Masks

In [1]:

  from spacr.core import preprocess_generate_masks
  %matplotlib inline

  settings = {
    'src':'/home/olafsson/datasets/plate1',
    'metadata_type':'cellvoyager',
    'custom_regex':None,
    'experiment':'screen',
    'channels':[0,1,2,3],
    'cell_channel':3,
    'cell_background':100,
    'cell_Signal_to_noise':10,
    'cell_CP_prob':-1,
    'remove_background_cell':False,
    'nucleus_channel':0,
    'nucleus_background':200,
    'nucleus_Signal_to_noise':5,
    'nucleus_CP_prob':0,
    'remove_background_nucleus':False,
    'pathogen_model':None,
    'pathogen_channel':2,
    'pathogen_background':400,
    'pathogen_Signal_to_noise':5,
    'pathogen_CP_prob':-2,
    'remove_background_pathogen':True,
    'consolidate':False,
    'magnification':40,
    'save':True,
    'preprocess':True,
    'masks':True,
    'batch_size':50,
    'filter':False,
    'merge_pathogens':False,
    'plot':True,
    'adjust_cells':True,
    'test_mode':True,
    'test_images':10,
    'random_test':True
  }
This code block sets up and launches the mask generation process in test mode using spaCR. Hover over each line to learn what it does.
channels
This slide demonstrates the initial mask generation using the Cellpose 'cyto' model. Note the high signal-to-noise ratio achieved by preprocessing with background removal and normalization enabled.
channels
Test mode is useful for validating segmentation settings before processing the entire dataset. This helps identify optimal Cellpose parameters like diameter, probability threshold, and SNR settings.
channels
Final results show successfully saved mask outputs. You can now proceed to object classification, visualization, or export for downstream spatial analyses.
channels
Test mode is useful for validating segmentation settings before processing the entire dataset. This helps identify optimal Cellpose parameters like diameter, probability threshold, and SNR settings.
channels
Test mode is useful for validating segmentation settings before processing the entire dataset. This helps identify optimal Cellpose parameters like diameter, probability threshold, and SNR settings.