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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json",
"version": "0.5.2",
"changelog": {
"0.5.2": "enhance metadata with improved descriptions",
"0.5.1": "update to huggingface hosting",
"0.5.0": "Fix transform usage",
"0.4.3": "README.md fix",
"0.4.2": "add name tag",
"0.4.1": "modify dataset key name",
"0.4.0": "update license files",
"0.3.0": "Update to scripts",
"0.2.0": "Unify naming",
"0.1.0": "Initial version"
},
"monai_version": "1.3.0",
"pytorch_version": "2.1.1",
"numpy_version": "1.25.2",
"optional_packages_version": {},
"name": "Valve Landmarks Regression",
"task": "Cardiac Valve Insertion Point Detection in Long-Axis MR Images",
"description": "A cardiac valve landmark detection model that localizes 10 valve insertion points throughout the cardiac cycle in long-axis MR images. The model processes 256x256 pixel images and outputs 2D coordinates for mitral, aortic, and tricuspid valve insertion points, enabling 3D finite element modeling for cardiac simulation.",
"authors": "Eric Kerfoot",
"copyright": "Copyright (c) Eric Kerfoot",
"references": [
"Kerfoot, E, King, CE, Ismail, T, Nordsletten, D & Miller, R 2021, Estimation of Cardiac Valve Annuli Motion with Deep Learning. https://doi.org/10.1007/978-3-030-68107-4_15"
],
"intended_use": "This is suitable for research purposes only",
"image_classes": "Single channel data, intensity scaled to [0, 1]",
"data_source": "Non-public dataset comprised of hand-annotated full cycle long axis MR images",
"coordinate_values": {
"0": 10,
"1": 15,
"2": 20,
"3": 25,
"4": 30,
"5": 35,
"6": 100,
"7": 150,
"8": 200,
"9": 250
},
"coordinate_meanings": {
"0": "mitral anterior 2CH",
"1": "mitral posterior 2CH",
"2": "mitral septal 3CH",
"3": "mitral free wall 3CH",
"4": "mitral septal 4CH",
"5": "mitral free wall 4CH",
"6": "aortic septal",
"7": "aortic free wall",
"8": "tricuspid septal",
"9": "tricuspid free wall"
},
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "magnitude",
"modality": "MR",
"num_channels": 1,
"spatial_shape": [
256,
256
],
"dtype": "float32",
"value_range": [],
"is_patch_data": false,
"channel_def": {
"0": "image"
}
}
},
"outputs": {
"pred": {
"type": "tuples",
"format": "points",
"num_channels": 2,
"spatial_shape": [
2,
10
],
"dtype": "float32",
"value_range": [],
"is_patch_data": false,
"channel_def": {
"0": "Y Dimension",
"1": "X Dimension"
}
}
}
}
}
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