| { |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", |
| "version": "0.0.4", |
| "changelog": { |
| "0.0.4": "enhance metadata with improved descriptions", |
| "0.0.3": "update to huggingface hosting", |
| "0.0.2": "Minor train.yaml clarifications", |
| "0.0.1": "Initial version" |
| }, |
| "monai_version": "1.4.0", |
| "pytorch_version": "2.4.0", |
| "numpy_version": "1.24.4", |
| "optional_packages_version": { |
| "nibabel": "5.2.1", |
| "pytorch-ignite": "0.4.11" |
| }, |
| "name": "Medical Image Segmentation Template", |
| "task": "Template for 3D Medical Image Segmentation", |
| "description": "A comprehensive 3D segmentation framework designed as a foundation for developing custom medical volumetric segmentation models. The template includes a configurable architecture and preprocessing pipeline, processing 128x128x128 voxel volumes with single-channel input and producing 4-class segmentation outputs. Includes support for random sphere generation for demonstration and testing purposes.", |
| "authors": "Eric Kerfoot", |
| "copyright": "Copyright (c) 2023 MONAI Consortium", |
| "network_data_format": { |
| "inputs": { |
| "image": { |
| "type": "image", |
| "format": "magnitude", |
| "modality": "none", |
| "num_channels": 1, |
| "spatial_shape": [ |
| 128, |
| 128, |
| 128 |
| ], |
| "dtype": "float32", |
| "value_range": [], |
| "is_patch_data": false, |
| "channel_def": { |
| "0": "image" |
| } |
| } |
| }, |
| "outputs": { |
| "pred": { |
| "type": "image", |
| "format": "segmentation", |
| "num_channels": 4, |
| "spatial_shape": [ |
| 128, |
| 128, |
| 128 |
| ], |
| "dtype": "float32", |
| "value_range": [ |
| 0, |
| 3 |
| ], |
| "is_patch_data": false, |
| "channel_def": { |
| "0": "background", |
| "1": "category 1", |
| "2": "category 2", |
| "3": "category 3" |
| } |
| } |
| } |
| } |
| } |
|
|
