{ "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" } } } } }