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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.0.4",
"changelog": {
"0.0.4": "enhance metadata with improved descriptions",
"0.0.3": "update to huggingface hosting",
"0.0.2": "Minor train.yaml clarifications",
"0.0.1": "Initial version"
},
"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"
},
"name": "Medical Image Segmentation Template",
"task": "Template for 3D Medical Image Segmentation",
"description": "A comprehensive 3D segmentation framework designed as a foundation for developing custom medical volumetric segmentation models. The template includes a configurable architecture and preprocessing pipeline, processing 128x128x128 voxel volumes with single-channel input and producing 4-class segmentation outputs. Includes support for random sphere generation for demonstration and testing purposes.",
"authors": "Eric Kerfoot",
"copyright": "Copyright (c) 2023 MONAI Consortium",
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "magnitude",
"modality": "none",
"num_channels": 1,
"spatial_shape": [
128,
128,
128
],
"dtype": "float32",
"value_range": [],
"is_patch_data": false,
"channel_def": {
"0": "image"
}
}
},
"outputs": {
"pred": {
"type": "image",
"format": "segmentation",
"num_channels": 4,
"spatial_shape": [
128,
128,
128
],
"dtype": "float32",
"value_range": [
0,
3
],
"is_patch_data": false,
"channel_def": {
"0": "background",
"1": "category 1",
"2": "category 2",
"3": "category 3"
}
}
}
}
}
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