| { | |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", | |
| "version": "0.3.2", | |
| "changelog": { | |
| "0.3.2": "add name tag", | |
| "0.3.1": "fix license Copyright error", | |
| "0.3.0": "update license files", | |
| "0.2.0": "unify naming", | |
| "0.1.1": "add torchscript model", | |
| "0.1.0": "complete the model package" | |
| }, | |
| "monai_version": "0.9.1", | |
| "pytorch_version": "1.11.0", | |
| "numpy_version": "1.22.3", | |
| "optional_packages_version": { | |
| "nibabel": "3.2.2", | |
| "itk": "5.2.1", | |
| "pytorch-ignite": "0.4.9", | |
| "pandas": "1.4.2" | |
| }, | |
| "name": "Prostate MRI anatomy", | |
| "task": "Segmentation of peripheral zone and central gland in prostate MRI", | |
| "description": "A pre-trained model for volumetric (3D) segmentation of the prostate from MRI images", | |
| "authors": "Keno Bressem", | |
| "copyright": "Copyright (c) Keno Bressem", | |
| "data_source": "Prostate158 from 10.5281/zenodo.6481141", | |
| "data_type": "nifti", | |
| "image_classes": "single channel data, intensity scaled to [0, 1]", | |
| "label_classes": "singe channel data, 1 central gland, 2 periheral zone, 0 is everything else", | |
| "pred_classes": "3 channels OneHot data, channel 1 central gland, channel 2 is peripheral zone, channel 0 is background", | |
| "eval_metrics": { | |
| "mean_dice": { | |
| "central gland": 0.88, | |
| "peripheral zone": 0.75 | |
| } | |
| }, | |
| "intended_use": "This is an example, not to be used for diagnostic purposes", | |
| "references": [ | |
| "Adams, L. C., Makowski, M. R., Engel, G., Rattunde, M., Busch, F., Asbach, P., ... & Bressem, K. K. (2022). Prostate158-An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection. Computers in Biology and Medicine, 148, 105817." | |
| ], | |
| "network_data_format": { | |
| "inputs": { | |
| "image": { | |
| "type": "image", | |
| "format": "magnitude", | |
| "modality": "MR", | |
| "num_channels": 1, | |
| "spatial_shape": [ | |
| 96, | |
| 96, | |
| 96 | |
| ], | |
| "dtype": "float32", | |
| "value_range": [ | |
| 0, | |
| 1 | |
| ], | |
| "is_patch_data": true, | |
| "channel_def": { | |
| "0": "image" | |
| } | |
| } | |
| }, | |
| "outputs": { | |
| "pred": { | |
| "type": "image", | |
| "format": "labels", | |
| "num_channels": 3, | |
| "spatial_shape": [ | |
| 96, | |
| 96, | |
| 96 | |
| ], | |
| "dtype": "float32", | |
| "value_range": [], | |
| "is_patch_data": true, | |
| "channel_def": { | |
| "0": "background", | |
| "1": "central gland", | |
| "2": "peripheral zone" | |
| } | |
| } | |
| } | |
| } | |
| } | |