{
  "model_type": "vesselboost-unet3d",
  "framework": "pytorch",
  "task": "image-segmentation",
  "architecture": {
    "class_name": "models.unet_3d.Unet",
    "spatial_dimensions": 3,
    "input_channels": 1,
    "output_channels": 1,
    "base_filters": 16,
    "encoder_stages": 4,
    "decoder_stages": 4,
    "convolution_kernel_size": [3, 3, 3],
    "output_kernel_size": [1, 1, 1],
    "normalization": "batch_norm_3d",
    "activation": "relu",
    "output_activation": "sigmoid_at_inference"
  },
  "input": {
    "format": "NIfTI",
    "contrasts": [
      "TOF-MRA",
      "T2*-weighted MRI (experimental checkpoint only)"
    ],
    "patch_size": [64, 64, 64],
    "default_patch_stride": [64, 64, 64]
  },
  "preprocessing": {
    "resize_target": "each spatial dimension is at least 64 and a multiple of 64",
    "resize_interpolation": "nearest_neighbor",
    "intensity_transform": {
      "name": "z_score_standardization",
      "scope": "whole_volume",
      "formula": "(x - mean(x)) / std(x)",
      "constant_volume_result": "zeros"
    },
    "optional_operations": [
      "N4 bias-field correction",
      "denoising",
      "brain extraction with separately distributed SynthStrip weights"
    ]
  },
  "postprocessing": {
    "probability_threshold": 0.1,
    "connected_component_minimum_voxels": 10,
    "connected_component_connectivity": 26,
    "prediction_resize_interpolation": "nearest_neighbor"
  },
  "checkpoint_format": "PyTorch state_dict ZIP serialization",
  "compatible_vesselboost": {
    "version": "2.0.2",
    "git_tag": "v2.0.2",
    "git_commit": "1504b00c91777d5e2c271c1cab7f500078f08c69",
    "source_url": "https://github.com/KMarshallX/VesselBoost/tree/v2.0.2"
  }
}