{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
    "version": "0.0.4",
    "changelog": {
        "0.0.4": "enhance metadata with improved descriptions",
        "0.0.3": "update to huggingface hosting",
        "0.0.2": "Minor train.yaml clarifications",
        "0.0.1": "Initial version"
    },
    "monai_version": "1.4.0",
    "pytorch_version": "2.4.0",
    "numpy_version": "1.24.4",
    "optional_packages_version": {
        "nibabel": "5.2.1",
        "pytorch-ignite": "0.4.11"
    },
    "name": "Medical Image Segmentation Template",
    "task": "Template for 3D Medical Image Segmentation",
    "description": "A comprehensive 3D segmentation framework designed as a foundation for developing custom medical volumetric segmentation models. The template includes a configurable architecture and preprocessing pipeline, processing 128x128x128 voxel volumes with single-channel input and producing 4-class segmentation outputs. Includes support for random sphere generation for demonstration and testing purposes.",
    "authors": "Eric Kerfoot",
    "copyright": "Copyright (c) 2023 MONAI Consortium",
    "network_data_format": {
        "inputs": {
            "image": {
                "type": "image",
                "format": "magnitude",
                "modality": "none",
                "num_channels": 1,
                "spatial_shape": [
                    128,
                    128,
                    128
                ],
                "dtype": "float32",
                "value_range": [],
                "is_patch_data": false,
                "channel_def": {
                    "0": "image"
                }
            }
        },
        "outputs": {
            "pred": {
                "type": "image",
                "format": "segmentation",
                "num_channels": 4,
                "spatial_shape": [
                    128,
                    128,
                    128
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    3
                ],
                "is_patch_data": false,
                "channel_def": {
                    "0": "background",
                    "1": "category 1",
                    "2": "category 2",
                    "3": "category 3"
                }
            }
        }
    }
}