{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
    "version": "0.3.6",
    "changelog": {
        "0.3.6": "added train/val graphs",
        "0.3.5": "update prepare datalist function",
        "0.3.4": "update output format of inference",
        "0.3.3": "update to use monai 1.0.1",
        "0.3.2": "enhance readme on commands example",
        "0.3.1": "fix license Copyright error",
        "0.3.0": "update license files",
        "0.2.1": "fix network_data_format error",
        "0.2.0": "unify naming",
        "0.1.1": "update for MetaTensor",
        "0.1.0": "complete the model package"
    },
    "monai_version": "1.0.1",
    "pytorch_version": "1.13.0",
    "numpy_version": "1.22.4",
    "optional_packages_version": {
        "nibabel": "4.0.1",
        "pytorch-ignite": "0.4.9",
        "scikit-learn": "1.1.3",
        "tensorboard": "2.10.1"
    },
    "task": "Multimodal Brain Tumor segmentation",
    "description": "A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data",
    "authors": "MONAI team",
    "copyright": "Copyright (c) MONAI Consortium",
    "data_source": "https://www.med.upenn.edu/sbia/brats2018/data.html",
    "data_type": "nibabel",
    "image_classes": "4 channel data, T1c, T1, T2, FLAIR at 1x1x1 mm",
    "label_classes": "3 channel data, channel 0 for Tumor core, channel 1 for Whole tumor, channel 2 for Enhancing tumor",
    "pred_classes": "3 channels data, same as label_classes",
    "eval_metrics": {
        "val_mean_dice": 0.8518,
        "val_mean_dice_tc": 0.8559,
        "val_mean_dice_wt": 0.9026,
        "val_mean_dice_et": 0.7905
    },
    "intended_use": "This is an example, not to be used for diagnostic purposes",
    "references": [
        "Myronenko, Andriy. '3D MRI brain tumor segmentation using autoencoder regularization.' International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654"
    ],
    "network_data_format": {
        "inputs": {
            "image": {
                "type": "image",
                "format": "magnitude",
                "modality": "MR",
                "num_channels": 4,
                "spatial_shape": [
                    "8*n",
                    "8*n",
                    "8*n"
                ],
                "dtype": "float32",
                "value_range": [],
                "is_patch_data": true,
                "channel_def": {
                    "0": "T1c",
                    "1": "T1",
                    "2": "T2",
                    "3": "FLAIR"
                }
            }
        },
        "outputs": {
            "pred": {
                "type": "image",
                "format": "segmentation",
                "num_channels": 3,
                "spatial_shape": [
                    "8*n",
                    "8*n",
                    "8*n"
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1
                ],
                "is_patch_data": true,
                "channel_def": {
                    "0": "Tumor core",
                    "1": "Whole tumor",
                    "2": "Enhancing tumor"
                }
            }
        }
    }
}