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MONAI

AI Segmentation 📥 Download Script

MONAI segmentation tutorial script.

Performs MONAI segmentation on the first volume in the opened project, splits a multilabel result into individual masks, keeps the largest label, filters to the largest connected region, generates a 3D preview, converts it to a surface, and performs simple surface cleanup and export.

Model licensing: the MONAI framework is Apache-2.0, but each Model Zoo bundle is separately licensed by whoever trained it, and some restrict commercial use. Check the bundle's own license before relying on its output. Volvicon supplies no bundles and grants no rights in them. See the AI Segmentation documentation, section "Framework and Model Licensing".

Complete Example​

# Import necessary modules
import ScriptingApi as api

# Instantiate the Application class
# The Application class serves as the primary entry point for interacting with
# the software. To begin using the API, create an instance of this class.
# Use the original class name `Application` or the pythonic alias `app` or `application`
# as the variable name to improve IntelliSense support. For details, refer to the documentation.
app = api.Application()

# Close any open project
app.close_project()

# Open a specific project
project_path = R'C:\Data\MyProject.vvcx'
app.open_project(project_path)

# Get the name of the first volume
volumes = app.get_all_volume_names()
if len(volumes) == 0:
   raise RuntimeError("No volumes found in project.")
volume_name = volumes[0]
print("Using volume:", volume_name)

# Get AI segmentation instance
# Use the pythonic alias `ai_segmentation` as the variable name to improve IntelliSense support.
# Hover over the variable to view available methods and properties.
# If no tooltip appears, IntelliSense does not recognize the variable name.
# For details, refer to the documentation.
ai_segmentation = app.get_ai_segmentation()

# Set the AI segmentation model type
ai_segmentation.set_model_type(api.AiSegmentationModelType.Monai)

# Check installation status and install if necessary
installed = ai_segmentation.get_installation_status()
if not installed:
    print('Installation required: The AI model is not installed.')
    print('Please run the application as an administrator to install it.')
    print('Attempting to install the AI model now.')
    installed = ai_segmentation.install_model()
    if not installed:
        raise RuntimeError('An error occurred during the installation of the AI model. The log file has been saved for troubleshooting.')
    else:
        print('The AI model is installed and ready for use.')
else:
    print('The AI model is installed and ready for use.')

# Configure MONAI parameters
monai_params: api.MonaiParams = ai_segmentation.get_default_monai_params()
monai_params.bundle_dir = 'C:/Users/XYZ/.monai/wholeBody_ct_segmentation'
monai_params.overlap = 0.8

# Some AI models may not support GPU execution. Use CPU to ensure compatibility,
# but prefer GPU when available for optimal performance.
monai_params.device = 'gpu'

# Run MONAI segmentation
mask_names = ai_segmentation.run_monai([volume_name], monai_params)
if len(mask_names) == 0:
    raise RuntimeError("Segmentation returned no masks (operation cancelled or failed).")
print("Generated masks:", mask_names)

# Get the name of the currently active mask
active_mask = app.get_active_mask_name()
print("Active mask:", active_mask)

# Get the mask_operations object to perform operations on masks
mask_operations = app.get_mask_operations()

# Split multi-label mask into individual masks and extract only the largest label by volume
splitted_masks = mask_operations.split_multi_label_mask(volume_name, active_mask, True, 1)
if len(splitted_masks) == 0:
   raise RuntimeError("Split operation did not produce separate labels.")
print("Splitted masks (largest label retained):", splitted_masks)

# Retain only the largest connected region
mask_operations.filter_regions(splitted_masks, True, 1, 8)

# Show only the splited mask, hide the original multi-label mask
app.set_masks_visible([active_mask], False)
app.set_masks_visible(mask_names, True)

# Set 3D preview quality and generate preview of all visible masks
app.set_mask_3d_preview_quality(api.Mask3dPreviewQuality.Optimal)
app.generate_mask_3d_preview(app.get_visible_mask_names())

# Convert generated 3D preview to surface objects
surfaces = mask_operations.convert_3d_preview_to_surface_objects()
if len(surfaces) == 0:
    raise RuntimeError("No surface generated from 3D preview.")
print("Created surfaces:", surfaces)

# Get the name of the currently active surface
surface = app.get_active_surface_name()
print(surface)

# Get the surface_operations object to perform operations on surfaces
surface_operations = app.get_surface_operations()

# Filter surface to keep only the largest shell
surface_filter_shells_params = api.SurfaceFilterShellsParams()
surface_filter_shells_params.largest_shells = 1
surface_operations.filter_shells(surface, api.SurfaceFilterShellsMethod.LargestShells, surface_filter_shells_params)

# Remesh the surface for better quality
surface_operations.remesh([surface])

# Fix surface issues using diagnostics checks
surface_diagnostics_checks = api.SurfaceDiagnosticsChecks()
surface_operations.fix_surface(surface, surface_diagnostics_checks)

# Hide all volumes and masks to focus on the surface
app.set_volumes_visible(app.get_all_volume_names(), False)
app.set_masks_visible(app.get_all_mask_names(), False)

# Isolate the surface
app.isolate_surfaces([surface])

# Export the surface to disk in STL format
surface_operations.export_surface_to_disk(surface, 'C:/tmp/surface.stl')

# Set surface representation to Solid with Edges
surface_render_properties_operations = surface_operations.get_render_properties_operations()
surface_render_properties_operations.set_representation([surface], api.SurfaceRepresentation.SolidEdges)

print("MONAI tutorial completed successfully.")