{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
    "version": "0.4.6",
    "changelog": {
        "0.4.6": "enhance metadata with improved descriptions",
        "0.4.5": "update to huggingface hosting",
        "0.4.4": "initial bundle assemblage."
    },
    "monai_version": "1.3.0",
    "pytorch_version": "2.1.0",
    "numpy_version": "1.22.2",
    "optional_packages_version": {
        "fire": "0.4.0",
        "nibabel": "4.0.1",
        "pytorch-ignite": "0.4.11"
    },
    "name": "Pediatric Abdominal CT Segmentation",
    "task": "3D Segmentation of Liver, Spleen and Pancreas in Pediatric Abdominal CT",
    "description": "A 3D segmentation model for liver, spleen, and pancreas in pediatric abdominal CT images. The model processes 96x96x96 pixel patches and provides segmentation masks. Pre-trained on TotalSegmentator, TCIA and BTCV datasets and fine-tuned on Cincinnati Children's Healthy Pediatric Dataset.",
    "authors": "Cincinnati Children's (CCHMC) - CAIIR Center (https://www.cincinnatichildrens.org/research/divisions/r/radiology/labs/caiir)",
    "copyright": "Copyright (c) MONAI Consortium",
    "data_source": "TotalSegmentator, TCIA and BTCV dataset public data",
    "data_type": "nifti",
    "image_classes": "single channel 3D data HU thresholded and clipped to a range of 0 to 1",
    "label_classes": "single channel data, 1 is liver, 2 is spleen, 3 is pancreas and 0 is everything else",
    "pred_classes": "single channel data, 1 is liver, 2 is spleen, 3 is pancreas and 0 is everything else",
    "eval_metrics": {
        "TS_mean_dice": 0.9,
        "TCIA_mean_dice": 0.87,
        "CCHMC_mean_dice": 0.89
    },
    "intended_use": "Pediatric model - Validation on institutional data required before clinical use",
    "references": [
        "MedArxiv paper: url to be updated"
    ],
    "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": 4,
                "spatial_shape": [
                    96,
                    96,
                    96
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1,
                    2,
                    3
                ],
                "is_patch_data": true,
                "label_def": {
                    "0": "background",
                    "1": "liver",
                    "2": "spleen",
                    "3": "pancreas"
                }
            }
        }
    }
}