{ "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", "version": "0.3.9", "changelog": { "0.3.9": "use ITKreader to avoid mass logs at image loading", "0.3.8": "restructure readme to match updated template", "0.3.7": "Update metric in metadata", "0.3.6": "Update ckpt drive link", "0.3.5": "Update figure and benchmarking", "0.3.4": "Update figure link in readme", "0.3.3": "Update, verify MONAI 1.0.1 and Pytorch 1.13.0", "0.3.2": "enhance readme on commands example", "0.3.1": "fix license Copyright error", "0.3.0": "update license files", "0.2.0": "unify naming", "0.1.0": "complete the model package", "0.0.1": "initialize the model package structure" }, "monai_version": "1.0.1", "pytorch_version": "1.13.0", "numpy_version": "1.21.2", "optional_packages_version": { "nibabel": "3.2.1", "pytorch-ignite": "0.4.8", "einops": "0.4.1" }, "task": "BTCV multi-organ segmentation", "description": "A pre-trained model for volumetric (3D) multi-organ segmentation from CT image", "authors": "MONAI team", "copyright": "Copyright (c) MONAI Consortium", "data_source": "RawData.zip from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752/", "data_type": "nibabel", "image_classes": "single channel data, intensity scaled to [0, 1]", "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", "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", "eval_metrics": { "mean_dice": 0.8269 }, "intended_use": "This is an example, not to be used for diagnostic purposes", "references": [ "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.", "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." ], "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": 14, "spatial_shape": [ 96, 96, 96 ], "dtype": "float32", "value_range": [ 0, 1 ], "is_patch_data": true, "channel_def": { "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" } } } } }