| { |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", |
| "version": "1.2.0", |
| "changelog": { |
| "1.2.0": "UNet [64,128,256,512] with Dice+clDice loss at 0.6mm isotropic spacing; adds centerline extraction postprocessing.", |
| "1.1.0": "Adopted the unified dataset manifest pipeline and aligned helper scripts with the other bundles.", |
| "1.0.0": "SegResNet baseline for binary coronary segmentation" |
| }, |
| "monai_version": "1.3.0", |
| "pytorch_version": "2.2.1", |
| "numpy_version": "1.26.4", |
| "optional_packages_version": { |
| "fire": "0.6.0", |
| "pytorch-ignite": "0.4.11", |
| "nibabel": "5.2.1", |
| "torchvision": "0.17.1", |
| "pandas": "2.2.1", |
| "monailabel": "0.8.1" |
| }, |
| "task": "Segmentation of coronary arteries in CCTA", |
| "description": "UNet trained to produce binary coronary tree masks from CCTA at 0.6mm isotropic spacing, with Dice+clDice loss for topology preservation.", |
| "authors": "Keno Bressem", |
| "copyright": "Copyright (c) Keno Bressem", |
| "network_data_format": { |
| "inputs": { |
| "image": { |
| "type": "image", |
| "format": "density", |
| "modality": "CT", |
| "num_channels": 1, |
| "spatial_shape": [ |
| 128, |
| 128, |
| 128 |
| ], |
| "dtype": "float32", |
| "value_range": [ |
| -1, |
| 1 |
| ], |
| "is_patch_data": true, |
| "channel_def": { |
| "0": "CT input Sequence" |
| } |
| } |
| }, |
| "outputs": { |
| "pred": { |
| "type": "image", |
| "format": "segmentation", |
| "num_channels": 2, |
| "spatial_shape": [ |
| 128, |
| 128, |
| 128 |
| ], |
| "dtype": "float32", |
| "value_range": [ |
| 0, |
| 1 |
| ], |
| "is_patch_data": true, |
| "channel_def": { |
| "0": "background", |
| "1": "coronary" |
| } |
| } |
| } |
| } |
| } |
|
|
