| { |
| "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", |
| "version": "0.2.3", |
| "changelog": { |
| "0.2.3": "enhance metadata with improved descriptions", |
| "0.2.2": "update to huggingface hosting", |
| "0.2.1": "fix pytype error", |
| "0.2.0": "set image_only to False", |
| "0.1.0": "complete the model package" |
| }, |
| "monai_version": "1.3.0", |
| "pytorch_version": "1.13.1", |
| "numpy_version": "1.24.3", |
| "optional_packages_version": { |
| "nibabel": "5.1.0", |
| "pytorch-ignite": "0.4.11", |
| "einops": "0.6.1", |
| "fire": "0.5.0", |
| "torchvision": "0.14.1", |
| "tensorboard": "2.17.0", |
| "scipy": "1.13.1" |
| }, |
| "name": "Renal Structures CECT Segmentation", |
| "task": "Multi-class Segmentation of Renal Structures in Contrast-Enhanced CT", |
| "description": "A 3D UNet-based segmentation model for comprehensive renal structure analysis in contrast-enhanced CT scans. The model processes 96x96x96 voxel patches and identifies six anatomical structures: arteries, veins, ureters, parenchyma, cysts, and tumors.", |
| "authors": "Sechenov university", |
| "copyright": "Copyright (c) Sechenov university", |
| "data_source": "AVUCTK_cases.zip", |
| "data_type": "nibabel", |
| "image_classes": "three channel data, intensity scaled to [0, 1]", |
| "label_classes": "1: artery, 2: vein, 3: ureter, 4: cyst, 5: tumor, 6: parenchyma", |
| "pred_classes": "1: artery, 2: vein, 3: ureter, 4: neoplasm, 5: parenchyma", |
| "eval_metrics": { |
| "mean_dice": 0.79 |
| }, |
| "intended_use": "This is PoC, not to be used for diagnostic purposes", |
| "references": [ |
| "Chernenkiy I. M. et al. Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network //Sechenov Medical Journal. \u2013 2023. \u2013 \u0422. 14. \u2013 \u2116. 1. \u2013 \u0421. 39-49. URL - https://www.sechenovmedj.com/jour/article/view/899" |
| ], |
| "network_data_format": { |
| "inputs": { |
| "image": { |
| "type": "image", |
| "format": "hounsfield", |
| "modality": "CT", |
| "num_channels": 3, |
| "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": 6, |
| "spatial_shape": [ |
| 96, |
| 96, |
| 96 |
| ], |
| "dtype": "float32", |
| "value_range": [ |
| 0, |
| 1 |
| ], |
| "is_patch_data": true, |
| "channel_def": { |
| "0": "background", |
| "1": "artery", |
| "2": "vein", |
| "3": "ureter", |
| "4": "neoplasm", |
| "5": "parenchyma" |
| } |
| } |
| } |
| } |
| } |
|
|
