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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.2.7",
"changelog": {
"0.2.7": "enhance metadata with improved descriptions",
"0.2.6": "update to huggingface hosting",
"0.2.5": "update large files",
"0.2.4": "fix black 24.1 format error",
"0.2.3": "update AddChanneld with EnsureChannelFirstd and remove meta_dict",
"0.2.2": "add name tag",
"0.2.1": "fix license Copyright error",
"0.2.0": "update license files",
"0.1.3": "Add training pipeline for fine-tuning models, support MONAI Label active learning",
"0.1.2": "fixed the dimension in convolution according to MONAI 1.0 update",
"0.1.1": "fixed the model state dict name",
"0.1.0": "complete the model package"
},
"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",
"einops": "0.7.0",
"fire": "0.6.0",
"timm": "0.6.7",
"torchvision": "0.19.0",
"tensorboard": "2.17.0"
},
"name": "Renal Structures UNEST Segmentation",
"task": "Kidney Structure Segmentation in CT Images",
"description": "A transformer-based 3D segmentation model that delineates kidney cortex, medulla, and pelvicalyceal system in CT images. The model processes 96x96x96 pixel patches and provides segmentation masks for detailed morphological analysis.",
"authors": "Vanderbilt University + MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "RawData.zip",
"data_type": "nibabel",
"image_classes": "single channel data, intensity scaled to [0, 1]",
"label_classes": "1: Kideny Cortex, 2:Medulla, 3:Pelvicalyceal system",
"pred_classes": "1: Kideny Cortex, 2:Medulla, 3:Pelvicalyceal system",
"eval_metrics": {
"mean_dice": 0.85
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Tang, Yucheng, et al. 'Self-supervised pre-training of swin transformers for 3d medical image analysis. arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791."
],
"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
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "kidney cortex",
"2": "medulla",
"3": "pelvicalyceal system"
}
}
}
}
}
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