{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
    "version": "0.0.6",
    "changelog": {
        "0.0.6": "enhance metadata with improved descriptions",
        "0.0.5": "update to huggingface hosting",
        "0.0.4": "Set image_only to False",
        "0.0.3": "Update for stable MONAI version",
        "0.0.2": "Retrain with new MONAI",
        "0.0.1": "initialize the model package structure"
    },
    "monai_version": "1.3.0",
    "pytorch_version": "1.13.1",
    "numpy_version": "1.22.2",
    "required_packages_version": {
        "fire": "0.5.0",
        "nibabel": "5.1.0",
        "pytorch-ignite": "0.4.11",
        "pyyaml": "6.0.2"
    },
    "supported_apps": {},
    "name": "Multi-organ Abdominal Segmentation",
    "task": "Multi-organ Segmentation in Abdominal CT Images",
    "description": "A 3D segmentation model optimized through Neural Architecture Search (DiNTS) that processes 96x96x96 pixel patches from CT scans to segment eight abdominal organs and structures. The model achieves a mean Dice score of 0.88 across all structures, including liver, spleen, pancreas, stomach, gallbladder, and vascular structures (artery and portal vein).",
    "authors": "Chen Shen, Holger R. Roth, Kazunari Misawa, Kensaku Mori",
    "copyright": "",
    "data_source": "Aichi Cancer Center, Japan",
    "data_type": "nibabel",
    "image_classes": "single channel data, intensity scaled to [0, 1]",
    "label_classes": "eight channels data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is everything else",
    "pred_classes": "8 channels OneHot data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is background",
    "eval_metrics": {
        "mean_dice": 0.88
    },
    "intended_use": "This is an example, not to be used for diagnostic purposes",
    "references": [
        "He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).",
        "Roth, H., Shen C, Oda H., Sugino T., Oda M., Hayashi Y., Misawa K., Mori K., 2018. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. International conference on medical image computing and computer-assisted intervention",
        "Shen, C., Roth, H. R., Nath, V., Hayashi, Y., Oda, M., Misawa, K., Mori, K., 2022. Effective hyperparameter optimization with proxy data for multi-organ segmentation. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 200-206)"
    ],
    "network_data_format": {
        "inputs": {
            "image": {
                "type": "image",
                "format": "hounsfield",
                "modality": "CT",
                "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": "segmentation",
                "num_channels": 8,
                "spatial_shape": [
                    96,
                    96,
                    96
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1,
                    2,
                    3,
                    4,
                    5,
                    6,
                    7
                ],
                "is_patch_data": true,
                "channel_def": {
                    "0": "background",
                    "1": "artery",
                    "2": "portal vein",
                    "3": "liver",
                    "4": "spleen",
                    "5": "stomach",
                    "6": "gallbladder",
                    "7": "pancreas"
                }
            }
        }
    }
}