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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.5.4",
"changelog": {
"0.5.4": "enhanced metadata with improved descriptions and task specification",
"0.5.3": "update to huggingface hosting",
"0.5.2": "use monai 1.4 and update large files",
"0.5.1": "update to use monai 1.3.1",
"0.5.0": "add load_pretrain flag for infer",
"0.4.9": "add checkpoint loader for infer",
"0.4.8": "fix the wrong GPU index issue of multi-node",
"0.4.7": "enhance prepare datalist file",
"0.4.6": "add dataset dir example",
"0.4.5": "update ONNX-TensorRT descriptions",
"0.4.4": "update error links",
"0.4.3": "add the ONNX-TensorRT way of model conversion",
"0.4.2": "fix mgpu finalize issue",
"0.4.1": "add non-deterministic note",
"0.4.0": "adapt to BundleWorkflow interface",
"0.3.9": "black autofix format and add name tag",
"0.3.8": "modify dataset key name",
"0.3.7": "restructure readme to match updated template",
"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": "1.4.0",
"pytorch_version": "2.4.0",
"numpy_version": "1.24.4",
"required_packages_version": {
"nibabel": "5.2.1",
"pytorch-ignite": "0.4.11",
"scikit-learn": "1.5.1",
"tensorboard": "2.17.0"
},
"supported_apps": {},
"name": "BraTS MRI segmentation",
"task": "Multimodal Brain Tumor Subregion Segmentation",
"description": "3D segmentation model for delineating brain tumor subregions from multimodal MRI scans (T1, T1c, T2, FLAIR). The model processes 4-channel input volumes with 1mm isotropic resolution and outputs 3-channel segmentation masks for tumor core (TC), whole tumor (WT), and enhancing tumor (ET).",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "BraTS 2018 Challenge Dataset (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"
}
}
}
}
}
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