{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
    "version": "0.3.2",
    "changelog": {
        "0.3.2": "add name tag",
        "0.3.1": "fix license Copyright error",
        "0.3.0": "update license files",
        "0.2.0": "unify naming",
        "0.1.1": "add torchscript model",
        "0.1.0": "complete the model package"
    },
    "monai_version": "0.9.1",
    "pytorch_version": "1.11.0",
    "numpy_version": "1.22.3",
    "optional_packages_version": {
        "nibabel": "3.2.2",
        "itk": "5.2.1",
        "pytorch-ignite": "0.4.9",
        "pandas": "1.4.2"
    },
    "name": "Prostate MRI anatomy",
    "task": "Segmentation of peripheral zone and central gland in prostate MRI",
    "description": "A pre-trained model for volumetric (3D) segmentation of the prostate from MRI images",
    "authors": "Keno Bressem",
    "copyright": "Copyright (c) Keno Bressem",
    "data_source": "Prostate158 from 10.5281/zenodo.6481141",
    "data_type": "nifti",
    "image_classes": "single channel data, intensity scaled to [0, 1]",
    "label_classes": "singe channel data, 1 central gland, 2 periheral zone, 0 is everything else",
    "pred_classes": "3 channels OneHot data, channel 1 central gland, channel 2 is peripheral zone, channel 0 is background",
    "eval_metrics": {
        "mean_dice": {
            "central gland": 0.88,
            "peripheral zone": 0.75
        }
    },
    "intended_use": "This is an example, not to be used for diagnostic purposes",
    "references": [
        "Adams, L. C., Makowski, M. R., Engel, G., Rattunde, M., Busch, F., Asbach, P., ... & Bressem, K. K. (2022). Prostate158-An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection. Computers in Biology and Medicine, 148, 105817."
    ],
    "network_data_format": {
        "inputs": {
            "image": {
                "type": "image",
                "format": "magnitude",
                "modality": "MR",
                "num_channels": 1,
                "spatial_shape": [
                    96,
                    96,
                    96
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1
                ],
                "is_patch_data": true,
                "channel_def": {
                    "0": "image"
                }
            }
        },
        "outputs": {
            "pred": {
                "type": "image",
                "format": "labels",
                "num_channels": 3,
                "spatial_shape": [
                    96,
                    96,
                    96
                ],
                "dtype": "float32",
                "value_range": [],
                "is_patch_data": true,
                "channel_def": {
                    "0": "background",
                    "1": "central gland",
                    "2": "peripheral zone"
                }
            }
        }
    }
}