| { | |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json", | |
| "version": "0.0.6", | |
| "changelog": { | |
| "0.0.6": "enhance metadata with improved descriptions", | |
| "0.0.5": "update to huggingface hosting", | |
| "0.0.4": "Set image_only to False", | |
| "0.0.3": "Update for stable MONAI version", | |
| "0.0.2": "Retrain with new MONAI", | |
| "0.0.1": "initialize the model package structure" | |
| }, | |
| "monai_version": "1.3.0", | |
| "pytorch_version": "1.13.1", | |
| "numpy_version": "1.22.2", | |
| "required_packages_version": { | |
| "fire": "0.5.0", | |
| "nibabel": "5.1.0", | |
| "pytorch-ignite": "0.4.11", | |
| "pyyaml": "6.0.2" | |
| }, | |
| "supported_apps": {}, | |
| "name": "Multi-organ Abdominal Segmentation", | |
| "task": "Multi-organ Segmentation in Abdominal CT Images", | |
| "description": "A 3D segmentation model optimized through Neural Architecture Search (DiNTS) that processes 96x96x96 pixel patches from CT scans to segment eight abdominal organs and structures. The model achieves a mean Dice score of 0.88 across all structures, including liver, spleen, pancreas, stomach, gallbladder, and vascular structures (artery and portal vein).", | |
| "authors": "Chen Shen, Holger R. Roth, Kazunari Misawa, Kensaku Mori", | |
| "copyright": "", | |
| "data_source": "Aichi Cancer Center, Japan", | |
| "data_type": "nibabel", | |
| "image_classes": "single channel data, intensity scaled to [0, 1]", | |
| "label_classes": "eight channels data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is everything else", | |
| "pred_classes": "8 channels OneHot data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is background", | |
| "eval_metrics": { | |
| "mean_dice": 0.88 | |
| }, | |
| "intended_use": "This is an example, not to be used for diagnostic purposes", | |
| "references": [ | |
| "He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).", | |
| "Roth, H., Shen C, Oda H., Sugino T., Oda M., Hayashi Y., Misawa K., Mori K., 2018. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. International conference on medical image computing and computer-assisted intervention", | |
| "Shen, C., Roth, H. R., Nath, V., Hayashi, Y., Oda, M., Misawa, K., Mori, K., 2022. Effective hyperparameter optimization with proxy data for multi-organ segmentation. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 200-206)" | |
| ], | |
| "network_data_format": { | |
| "inputs": { | |
| "image": { | |
| "type": "image", | |
| "format": "hounsfield", | |
| "modality": "CT", | |
| "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": "segmentation", | |
| "num_channels": 8, | |
| "spatial_shape": [ | |
| 96, | |
| 96, | |
| 96 | |
| ], | |
| "dtype": "float32", | |
| "value_range": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| 4, | |
| 5, | |
| 6, | |
| 7 | |
| ], | |
| "is_patch_data": true, | |
| "channel_def": { | |
| "0": "background", | |
| "1": "artery", | |
| "2": "portal vein", | |
| "3": "liver", | |
| "4": "spleen", | |
| "5": "stomach", | |
| "6": "gallbladder", | |
| "7": "pancreas" | |
| } | |
| } | |
| } | |
| } | |
| } | |