{
  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
  "version": "1.2.0",
  "changelog": {
    "1.2.0": "UNet [64,128,256,512] with Dice+clDice loss at 0.6mm isotropic spacing; adds centerline extraction postprocessing.",
    "1.1.0": "Adopted the unified dataset manifest pipeline and aligned helper scripts with the other bundles.",
    "1.0.0": "SegResNet baseline for binary coronary segmentation"
  },
  "monai_version": "1.3.0",
  "pytorch_version": "2.2.1",
  "numpy_version": "1.26.4",
  "optional_packages_version": {
    "fire": "0.6.0",
    "pytorch-ignite": "0.4.11",
    "nibabel": "5.2.1",
    "torchvision": "0.17.1",
    "pandas": "2.2.1",
    "monailabel": "0.8.1"
  },
  "task": "Segmentation of coronary arteries in CCTA",
  "description": "UNet trained to produce binary coronary tree masks from CCTA at 0.6mm isotropic spacing, with Dice+clDice loss for topology preservation.",
  "authors": "Keno Bressem",
  "copyright": "Copyright (c) Keno Bressem",
  "network_data_format": {
    "inputs": {
      "image": {
        "type": "image",
        "format": "density",
        "modality": "CT",
        "num_channels": 1,
        "spatial_shape": [
          128,
          128,
          128
        ],
        "dtype": "float32",
        "value_range": [
          -1,
          1
        ],
        "is_patch_data": true,
        "channel_def": {
          "0": "CT input Sequence"
        }
      }
    },
    "outputs": {
      "pred": {
        "type": "image",
        "format": "segmentation",
        "num_channels": 2,
        "spatial_shape": [
          128,
          128,
          128
        ],
        "dtype": "float32",
        "value_range": [
          0,
          1
        ],
        "is_patch_data": true,
        "channel_def": {
          "0": "background",
          "1": "coronary"
        }
      }
    }
  }
}