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Smart Interpolation

biomedisa.interpolation.smart_interpolation(
    data,
    labelData,
    nbrw=10,
    sorw=4000,
    compression=True,
    allaxis=False,
    denoise=False,
    uncertainty=False,
    ignore='none',
    only='all',
    smooth=0,
    platform=None,
    return_hits=False,
    acwe=False,
    acwe_alpha=1.0,
    acwe_smooth=1,
    acwe_steps=3,
    clean=None,
    fill=None
)

Parameters:

  • data : array_like

    Image data (must be three-dimensional).

  • labelData : array_like

    Pre-segmented slices (must be three dimensional). The non-segmented area has the value 0.

Returns:

  • out : dictionary

    Dictionary containing array-like objects for the results {'regular', 'smooth', 'uncertainty', 'hits'} when available.

Other Parameters (use leading -- for command-line, e.g. --help):

  • help: Show more information and exit (command-line only).
  • version: Show Biomedisa version (command-line only).
  • nbrw INT: Number of random walks starting at each pre-segmented pixel (default: 10).
  • sorw INT: Steps of a random walk (default: 4000).
  • compression: Compress segmentation results (default: True).
  • allaxis: If pre-segmentation is not exlusively in xy-plane (default: False).
  • denoise: Smooth/denoise image data before processing (default: False).
  • uncertainty: Return uncertainty of segmentation result (default: False).
  • ignore STR: Ignore specific label(s), e.g. "2,5,6" (default: none).
  • only STR: Segment only specific label(s), e.g. "1,3,5" (default: all).
  • smooth INT: Number of smoothing iterations for segmentation result (default: 0).
  • platform STR: One of "cuda", "opencl_NVIDIA_GPU", "opencl_Intel_CPU" (default: None).
  • return_hits: Return hits from each label. Only works for small image data (default: False).
  • acwe: Post-processing with active contour (default: False).
  • acwe_alpha FLOAT: Pushing force of active contour (default: 1.0).
  • acwe_smooth INT: Smoothing steps of active contour (default: 1).
  • acwe_steps INT: Iterations of active contour (default: 3).
  • clean FLOAT: Remove outliers, e.g. 0.5 means that objects smaller than 50 percent of the size of the largest object will be removed (default: None).
  • fill FLOAT: Fill holes, e.g. 0.5 means that all holes smaller than 50 percent of the entire label will be filled (default: None).

Multi-GPU (e.g. 4 GPUs)

mpiexec -np 4 python3 -m biomedisa.interpolation Downloads\NMB_F2875.tif Downloads\labels.NMB_F2875.tif

If you encounter GPU or host memory issues, you can split your volume into smaller segments and merge the results. For instance, you could use 8 sub-volumes

python -m biomedisa.features.split_volume Downloads\NMB_F2875.tif Downloads\labels.NMB_F2875.tif --split_x=2 --split_y=2 --split_z=2