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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.5.8",
"changelog": {
"0.5.8": "enhance metadata with improved descriptions",
"0.5.7": "update to huggingface hosting",
"0.5.6": "use monai 1.4 and update large files",
"0.5.5": "update to use monai 1.3.1",
"0.5.4": "add load_pretrain flag for infer",
"0.5.3": "update to use monai 1.3.0",
"0.5.2": "update the checkpoint loader logic for inference",
"0.5.1": "add option to validate at training start, and I/O param entries",
"0.5.0": "enable finetune and early stop",
"0.4.9": "fix orientation issue on clicks",
"0.4.8": "Add infer transforms to manage clicks from viewer",
"0.4.7": "fix the wrong GPU index issue of multi-node",
"0.4.6": "update to use rc7 which solves dynunet issue",
"0.4.5": "remove error dollar symbol in readme",
"0.4.4": "add RAM comsumption with Cachedataset",
"0.4.3": "update ONNX-TensorRT descriptions",
"0.4.2": "deterministic retrain benchmark, update fig links",
"0.4.1": "add the ONNX-TensorRT way of model conversion",
"0.4.0": "fix mgpu finalize issue",
"0.3.9": "enable deterministic training",
"0.3.8": "adapt to BundleWorkflow interface",
"0.3.7": "add name tag",
"0.3.6": "restructure readme to match updated template",
"0.3.5": "update metric in metadata",
"0.3.4": "add validate.json file and dice score in readme",
"0.3.3": "update to use monai 1.0.1",
"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": "1.4.0",
"pytorch_version": "2.4.0",
"numpy_version": "1.24.4",
"required_packages_version": {
"itk": "5.4.0",
"pytorch-ignite": "0.4.11",
"scikit-image": "0.23.2",
"einops": "0.7.0",
"tensorboard": "2.17.0",
"nibabel": "5.2.1"
},
"supported_apps": {},
"name": "Spleen DeepEdit Interactive Segmentation",
"task": "Interactive Spleen Segmentation in CT Images with Point-based Guidance",
"description": "An interactive 3D segmentation model that processes 128x128x128 pixel patches from CT scans to segment the spleen. The model incorporates user-provided point annotations through the DeepEdit framework. It accepts positive and negative click inputs to refine segmentation boundaries in real-time.",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "Task09_Spleen.tar from http://medicaldecathlon.com/",
"data_type": "nibabel",
"image_classes": "Three channel input: channel 0: CT image scaled to [0, 1], channels 1-2: positive and negative click maps",
"label_classes": "Single channel binary mask: 1: spleen, 0: background",
"pred_classes": "2 channels OneHot data, channel 1 is spleen, channel 0 is background",
"eval_metrics": {
"mean_dice": 0.97
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Sakinis, Tomas, et al. 'Interactive segmentation of medical images through fully convolutional neural networks.' arXiv preprint arXiv:1903.08205 (2019)"
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "hounsfield",
"modality": "CT",
"num_channels": 3,
"spatial_shape": [
128,
128,
128
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "image"
}
}
},
"outputs": {
"pred": {
"type": "image",
"format": "segmentation",
"num_channels": 2,
"spatial_shape": [
128,
128,
128
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "background",
"1": "spleen"
}
}
}
}
}
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