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{ |
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", |
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"version": "0.3.9", |
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"changelog": { |
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"0.3.9": "use ITKreader to avoid mass logs at image loading", |
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"0.3.8": "restructure readme to match updated template", |
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"0.3.7": "Update metric in metadata", |
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"0.3.6": "Update ckpt drive link", |
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"0.3.5": "Update figure and benchmarking", |
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"0.3.4": "Update figure link in readme", |
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"0.3.3": "Update, verify MONAI 1.0.1 and Pytorch 1.13.0", |
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"0.3.2": "enhance readme on commands example", |
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"0.3.1": "fix license Copyright error", |
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"0.3.0": "update license files", |
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"0.2.0": "unify naming", |
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"0.1.0": "complete the model package", |
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"0.0.1": "initialize the model package structure" |
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}, |
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"monai_version": "1.0.1", |
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"pytorch_version": "1.13.0", |
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"numpy_version": "1.21.2", |
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"optional_packages_version": { |
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"nibabel": "3.2.1", |
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"pytorch-ignite": "0.4.8", |
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"einops": "0.4.1" |
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}, |
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"task": "BTCV multi-organ segmentation", |
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"description": "A pre-trained model for volumetric (3D) multi-organ segmentation from CT image", |
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"authors": "MONAI team", |
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"copyright": "Copyright (c) MONAI Consortium", |
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"data_source": "RawData.zip from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752/", |
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"data_type": "nibabel", |
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"image_classes": "single channel data, intensity scaled to [0, 1]", |
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"label_classes": "multi-channel data,0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland", |
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"pred_classes": "14 channels OneHot data, 0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland", |
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"eval_metrics": { |
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"mean_dice": 0.8269 |
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}, |
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"intended_use": "This is an example, not to be used for diagnostic purposes", |
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"references": [ |
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"Hatamizadeh, Ali, et al. 'Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.", |
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"Tang, Yucheng, et al. 'Self-supervised pre-training of swin transformers for 3d medical image analysis. arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791." |
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], |
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"network_data_format": { |
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"inputs": { |
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"image": { |
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"type": "image", |
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"format": "hounsfield", |
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"modality": "CT", |
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"num_channels": 1, |
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"spatial_shape": [ |
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96, |
|
96, |
|
96 |
|
], |
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"dtype": "float32", |
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"value_range": [ |
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0, |
|
1 |
|
], |
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"is_patch_data": true, |
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"channel_def": { |
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"0": "image" |
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} |
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} |
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}, |
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"outputs": { |
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"pred": { |
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"type": "image", |
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"format": "segmentation", |
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"num_channels": 14, |
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"spatial_shape": [ |
|
96, |
|
96, |
|
96 |
|
], |
|
"dtype": "float32", |
|
"value_range": [ |
|
0, |
|
1 |
|
], |
|
"is_patch_data": true, |
|
"channel_def": { |
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"0": "background", |
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"1": "spleen", |
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"2": "Right Kidney", |
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"3": "Left Kideny", |
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"4": "Gallbladder", |
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"5": "Esophagus", |
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"6": "Liver", |
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"7": "Stomach", |
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"8": "Aorta", |
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"9": "IVC", |
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"10": "Portal and Splenic Veins", |
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"11": "Pancreas", |
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"12": "Right adrenal gland", |
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"13": "Left adrenal gland" |
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} |
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} |
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} |
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} |
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} |
|
|