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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.0.1",
"changelog": {
"0.0.1": "Initial version"
},
"monai_version": "1.5.0",
"pytorch_version": "2.6.0",
"numpy_version": "1.26.4",
"optional_packages_version": {},
"required_packages_version": {
"setuptools": "75.8.0",
"opencv-python-headless": "4.11.0.86",
"pandas": "2.3.0",
"seaborn": "0.13.2",
"scikit-learn": "1.6.1",
"progressbar": "2.5",
"pydicom": "3.0.1",
"fire": "0.7.0",
"torchvision": "0.21.0",
"detectron2": "0.6",
"lxml": "5.4.0",
"pillow": "11.2.1"
},
"name": "retinalOCT_RPD_segmentation",
"task": "Reticular Pseudodrusen (RPD) instance segmentation.",
"description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.",
"authors": "Yelena Bagdasarova, Scott Song",
"copyright": "Copyright (c) 2022, uw-biomedical-ml",
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "magnitude",
"modality": "OCT",
"num_channels": 1,
"spatial_shape": [
496,
1024
],
"dtype": "int16",
"value_range": [
0,
256
],
"is_patch_data": false,
"channel_def": {
"0": "image"
}
}
},
"preprocessed_data_sources": {
"vol_file": {
"type": "image",
"format": "magnitude",
"modality": "OCT",
"num_channels": 1,
"spatial_shape": [
496,
1024,
"D"
],
"dtype": "int16",
"value_range": [
0,
256
],
"description": "The pixel array of each OCT slice is extracted with volreader and the png files saved to <extracted_dir>/<some>/<file>/<name>/<some_file_name>_oct_<DDD>.png on disk, where <DDD> is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. "
},
"dicom_series": {
"type": "image",
"format": "magnitude",
"modality": "OCT",
"SOP class UID": "1.2.840.10008.5.1.4.1.1.77.1.5.4",
"num_channels": 1,
"spatial_shape": [
496,
1024,
"D"
],
"dtype": "int16",
"value_range": [
0,
256
],
"description": "The pixel array of each OCT slice is extracted with pydicom and the png files saved to <extracted_dir>/<SOPInstanceUID>/<SOPInstanceUID>_oct_<DDD>.png on disk, where <DDD> is the slice number. "
}
},
"outputs": {
"pred": {
"dtype": "dictionary",
"type": "dictionary",
"format": "COCO",
"modality": "n/a",
"value_range": [
0,
1
],
"num_channels": 1,
"spatial_shape": [
496,
1024
],
"channel_def": {
"0": "RPD"
},
"description": "This output is a JSON file in COCO Instance Segmentation format, containing bounding boxes, segmentation masks, and output probabilities for detected instances."
}
},
"post_processed_outputs": {
"binary segmentation": {
"type": "image",
"format": "TIFF",
"modality": "OCT",
"num_channels": 3,
"spatial_shape": [
496,
1024
],
"description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a binary segmentation mask for a single OCT slice from the input volume. The segmentation masks are stacked in the same order as the original OCT slices."
},
"binary segmentation overlay": {
"type": "image",
"format": "TIFF",
"modality": "OCT",
"num_channels": 3,
"spatial_shape": [
496,
1024
],
"description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask."
},
"instance segmentation overlay": {
"type": "image",
"format": "TIFF",
"modality": "OCT",
"num_channels": 3,
"spatial_shape": [
496,
1024
],
"description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask."
}
}
}
}
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