ProstateSegmenterLegacy.json

{
  "name": "Prostate Gland Segmenter Legacy",
  "number_of_inputs": 1,
  "task": "Segmentation.",
  "organ": "Prostate",
  "modality": "MRI",
  "train_test_data_details": "The model is trained on pelvic MRI scans of a 3T machine without use of an Endorectal Coil.",
  "briefdescription": "Whole-gland prostate segmentation in pelvic MRI scans.",
  "detaileddescription": "",
  "version": "0.1",
  "docker":
    {
    "dockerhub_repository": "deepinfer/prostate-segmenter-cpu",
    "digest": "sha256:43f13084e4d75508a0bc08b4a8c55880b4a5defb90043cb82d6a39ac25adabbf",
    "size": "4.6 GB"
    }
  ,
  "training":
    {
      "number of subjects": 224
    }
  ,
  "test":
    {
      "number of subjects": 50,
      "Accuracy": 0.76,
      "Metric": "Dice Similarity"
    }
  ,
  "pixel_types": "BasicPixelIDTypeList",
  "vector_pixel_types_by_component": "VectorPixelIDTypeList",
  "members": [
    {
      "name": "InputVolume",
      "type": "volume",
      "iotype": "input",
      "voltype": "ScalarVolume",
      "default": "std::vector<unsigned int>(3, 64)",
      "itk_type": "typename FilterType::SizeType"
    },
    {
      "name": "OutputLabel",
      "type": "volume",
      "iotype": "output",
      "voltype": "LabelMap",
      "default": "std::vector<unsigned int>(3, 64)",
      "itk_type": "typename FilterType::SizeType"
    },
    {
      "name": "Accuracy",
      "type": "bool",
      "default" : "true",
      "iotype": "input"
    }

]
}