| { | |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json", | |
| "version": "0.4.3", | |
| "changelog": { | |
| "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": "1.0.1", | |
| "pytorch_version": "1.13.0", | |
| "numpy_version": "1.21.2", | |
| "optional_packages_version": {}, | |
| "name": "Valve landmarks regression", | |
| "task": "Given long axis MR images of the heart, identify valve insertion points through the full cardiac cycle", | |
| "description": "This network is used to find where valves attach to heart to help construct 3D FEM models for computation. The output is an array of 10 2D coordinates.", | |
| "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" | |
| } | |
| } | |
| } | |
| } | |
| } | |