| { | |
| "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" | |
| } | |
| } | |
| } | |
| } | |
| } | |