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microsoft / InnerEye-Gateway

The InnerEye-Gateway is a Windows service that acts as a DICOM end point to run inference on https://github.com/microsoft/InnerEye-DeepLearning models.
https://aka.ms/innereye
MIT License
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ai azure azureml dicom medical-imaging

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This project is now archived

This project is no longer under active maintenance. It is read-only, but you can still clone or fork the repo. Check here for further info. Please contact innereye_info@service.microsoft.com if you run into trouble with the "Archived" state of the repo.

InnerEye-Gateway

Overview

This document has instructions specially for InnerEye-Gateway https://github.com/microsoft/InnerEye-Gateway.

For InnerEye-Inference repo please visit this documentation.

The InnerEye-Gateway comprises Windows services that act as a DICOM Service Class Provider. After an Association Request to C-STORE a set of DICOM image files, these will be anonymised by removing a user-defined set of identifiers, and passed to a web service running InnerEye-Inference. Inference will then pass them to an instance of InnerEye-Deeplearning running on Azure to execute InnerEye-DeepLearning models. The result is downloaded, deanonymised and passed to a configurable DICOM destination. All DICOM image files, and the model output, are automatically deleted immediately after use.

The gateway should be installed on a machine within your DICOM network that is able to access a running instance of InnerEye-Inference.

environment

Contents

Getting Started

To get started with setting up this project you will need the following pre-requisites:

  1. A machine running Windows 10, because the Gateway runs as Windows Services. It requires a network card to act as a DICOM SCP and for access to the InnerEye-Inference web service. A large hard disk is not required because the DICOM image files are not kept for very long.

  2. Visual Studio 2019 Community Edition (Download Visual Studio Community Edition)

  3. .Net Framework 4.6.2 Download .Net Framework

  4. To clone the repository a git client with large file support, e.g. git for windows

  5. Wix Toolset for building the installer Download WixToolset

  6. Ensure you have run the download_dcmtk.ps1 PowerShell script to download two DICOM tools (DICOM Toolkit and Dicom3tools) required for testing the gateway. Note that these tools are only required for testing but the test projects and therefore the gateway will not build without them. To enable and run the script:

    1. Start PowerShell with the Run as administrator option.

    2. Run the PowerShell command:

      Set-ExecutionPolicy -ExecutionPolicy Unrestricted

    3. Agree to the change

    4. From the root dir, run the following command:

      .\Source\Microsoft.Gateway\download_dcmtk.ps1

    5. If you get an error similar to the following: Invoke-WebRequest : The remote server returned an error: (404) Not Found. then it's highly likely the Dicom3tools link is outdated. You'll need to navigate to dclunie.com, locate the latest work-in-progress copy of the windows dicom3tools (this link should be correct), copy the download link of the latest .zip file displayed, and replace the URL in line 5 of the download_dcmtk.ps1 script before running again (you may need to remove files downloaded on the failed run first).

  7. InnerEye-Inference service from https://github.com/microsoft/InnerEye-Inference running as a web service, using, for example Azure Web Services. Note the URI that the service has been deployed to and the license key stored in the environment variable CUSTOMCONNSTR_API_AUTH_SECRET on the InnerEye-Inference deployment, they are needed as explained below.

License Keys

For security in InnerEye-Gateway and InnerEye-Inference the license keys are stored in environment variables and never stored in JSON or other configuration files. InnerEye-Inference uses the environment variable CUSTOMCONNSTR_API_AUTH_SECRET whereas InnerEye-Gateway allows the name of the environment variable to be configured in the JSON file in the LicenseKeyEnvVar property. This is so that the tests may be configured to run against a different InnerEye-Inference web service. Note also that because the applications run as windows services the environment variables should be system variables, not user variables, so they can be accessed by the services.

Alongside LicenseKeyEnvVar the property InferenceUri holds the URI of a running instance of InnerEye-Inference and the environment variable identified by LicenseKeyEnvVar should hold the license key for that instance. The license key is a user-created string that is defined when you configure your inference service.

For example if InferenceUri is "https://myinnereyeinference.azurewebsites.net", LicenseKeyEnvVar is "MY_GATEWAY_API_AUTH_SECRET", and the contents of the environment variable CUSTOMCONNSTR_API_AUTH_SECRET used for "https://myinnereyeinference.azurewebsites.net" is MYINFERENCELICENSEKEY, then set this environment variable with the PowerShell command, running as administrator:

setx MY_GATEWAY_API_AUTH_SECRET MYINFERENCELICENSEKEY /M

To Build The Gateway

  1. Clone the repository.

  2. Download the DICOM tools using the PowerShell script in Getting Started.

  3. Open the project solution file ./Source/Microsoft.Gateway/Microsoft.Gateway.sln in Visual Studio 2019.

  4. Set the project platform to x64.

  5. Build the Solution.

To Run The Tests

  1. Run Visual Studio as Administrator, because one of the tests needs to access system environment variables.

  2. Build the gateway.

  3. For end to end tests:

    python InnerEye/ML/runner.py --azureml=True --model=PassThroughModel

  4. Make sure the testing Default Processor Architecture is set to x64. This can be checked by navigating to Test > Test Settings > Default Process Architecture

  5. The tests can be executed from Visual Studio 2019 by navigating to Test > Windows > Test Explorer.

  6. All the available tests will be visible in the Test Explorer.

  7. A test can be executed by right clicking on the test and selecting the Run Selected Tests option.

  8. To execute all the available tests, click on the Run All link in the Test Explorer.

  9. The log for the test execution can be found in the Output window.

To Run The Gateway In Development

  1. The gateway uses multiple startup projects: Microsoft.InnerEye.Listener.Processor and Microsoft.InnerEye.Listener.Receiver. Configure this in Visual Studio by right clicking on the Solution in Solution Explorer and selecting "Set Startup Projects...". Click the radio button "Multiple startup projects" and for the projects above select "Start" in the "Action" combo box.

  2. Check the configuration settings for Microsoft.InnerEye.Listener.Processor in ./Source/Microsoft.Gateway/SampleConfigurations/GatewayProcessorConfig.json. More details are in Processor Configuration, but the most important parameters to check are in ProcessorSettings:

    • InferenceUri is the Uri for the InnerEye-Inference web service from Getting Started. See License Keys for more details.

    • LicenseKeyEnvVar is the name of the environment variable which contains the license key for the InnerEye-Inference web service at InferenceUri. See License Keys for more details.

  3. Check the configuration settings for Microsoft.InnerEye.Listener.Receiver in ./Source/Microsoft.Gateway/SampleConfigurations/GatewayReceiveConfig.json. More details are in Receiver Configuration, but the most important parameter to check is:

    • Port in GatewayDicomEndPoint in ReceiveServiceConfig - holds the IP port the receiver service listens on.

  4. Check the configuration settings for the application entity models in the folder ./Source/Microsoft.Gateway/SampleConfigurations/GatewayModelRulesConfig. The configuration may be split across multiple JSON files, if desired, and the configuration will be merged. The structure of these files is that each must contain an array of the form:

[
  {
    "CallingAET": << Calling application entity title >>,
    "CalledAET": << Called application entity title >>,
    "AETConfig": {
      "Config": {
        "AETConfigType": << One of "Model", "ModelDryRun", or "ModelWithResultDryRun" >>
        "ModelsConfig": [
          {
            "ModelId": << Model id for model in inference service, e.g. "PassThroughModel:3" >>,
            "ChannelConstraints": [ << Array of channel constraints >> ],
            "TagReplacements": [ << Array of tag replacements >> ]
          },
          ...
        ]
      },
      "Destination": << Destination >>,
      "ShouldReturnImage": << Return image flag >>
    }
  }

All JSON files in this folder are loaded and parsed. If the same CallingAET and CalledAET are found in more than one instance then the ModelsConfig arrays are concatenated to create one instance sharing all the other properties (which are taken from the first instance found). More details are in Model Configuration.

To Manually Test The Gateway

The DCMTK - DICOM Toolkit can be used to push DICOM images into the gateway, and receive the segmented DICOM-RT file that is returned.

As described later in Model Configuration, the gateway can be configured to support multiple application entity models. For a manual test, pick one of these, which will be called the target ae model below.

storescp

The tool storescp can be used to store the segmented images. A sample Powershell script is provided here which will start storescp and wait for the segmented images to be returned from the gateway, and is copied below:

$ReceiveFolder = "TestReceived"
$AETitle = "PACS"
$Port = 104

if (-not(Test-Path $ReceiveFolder))
{
  New-Item $ReceiveFolder -ItemType Directory
}

& ".\dcmtk-3.6.5-win64-dynamic\bin\storescp.exe" `
  --log-level trace                 <# log level #> `
  --aetitle $AETitle                <# set my AE title #> `
  --output-directory $ReceiveFolder <# write received objects to existing directory TestReceived #> `
  $Port                             <# port #>

Here:

  1. $ReceiveFolder is a folder that will be used to store the returned segmented DICOM images (the sample script creates it if it does not already exist).
  2. $AETitle is the Application Entity Title for this application. In principle it should match the Title set in the Destination part of the target ae model, but in practice this is not validated.
  3. $Port is the port that this application will listen on. It must match the Port set in the Destination part of the target ae model.

storescu

The tool storescu can be used to send a set of DICOM images to the gateway for segmentation. A sample Powershell script is provided here which will send a folder of DICOM files to the gateway, and is copied below:

$AETitle = "RADIOMICS_APP"
$Call = "PassThroughModel"
$Port = 111
$SendFolder = "..\..\Images\HN\"

& ".\dcmtk-3.6.5-win64-dynamic\bin\storescu.exe" `
  --log-level trace                      <# log level #> `
  --scan-directories                     <# scan directories for input files #> `
  --scan-pattern "*.dcm"                 <# pattern for filename matching (wildcards) #> `
  --aetitle $AETitle                     <# set my calling AE title #> `
  --call $Call                           <# set called AE title of peer #> `
  127.0.0.1                              <# peer #> `
  $Port                                  <# port #> `
  $SendFolder                           <# dcmfile-in #>

Here:

  1. $AETitle is the calling Application Entity Title for this application. It should match the CallingAET set in the target ae model.
  2. $Call is the called Application Entity Title. It should match the CalledAET set in the target ae model.
  3. $Port is the port that the gateway is listening on, as configured in GatewayReceiveConfig.json in the ReceiveServiceConfig.GatewayDicomEndPoint.Port parameter.
  4. $SendFolder is the path to the folder of DICOM images to send to the gateway.

To Test

  1. Check the powershell script test_recv.ps1 and start it in one shell. Leave it running, it will print "T: Timeout while waiting for incoming network data".

  2. Start Visual Studio debugging, making sure that both projects Microsoft.InnerEye.Listener.Processor and Microsoft.InnerEye.Listener.Receiver are set to start.

  3. Check the powershell script test_push.ps1 and start it in another shell. This will send the folder of images in ./Images/HN/ to the gateway.

  4. Check that the ReceiveService of the Receiver application received the images.

  5. Check that the UploadService of the Processor application uploaded the images to the Inference service.

  6. Check that the DownloadService of the Processor application polled the Inference service for the segmentation result (this event will have "DownloadProgress": 50 until the segmentation is available).

  7. Check that the DeleteService deleted the received DICOM image files.

  8. Check that the DownloadService finally downloaded the segmenttion.

  9. Check that the PushService pushed the segmentation to storescp.

  10. Check that the DeleteService deleted the segmentation.

To Run The Gateway In Production

The WixToolset will build an installer when the gateway is built. For a release build it will be:

./Source/Microsoft.Gateway/Microsoft.InnerEye.Listener.Wix/bin/x64/Release/Microsoft.InnerEye.Gateway.msi

Note that the installer will include the configuration files in the folder ./Source/Microsoft.Gateway/SampleConfigurations, as well as the receiver and processor applications. Therefore the workflow should be to evolve the configuration files running the gateway as above and when the configuration files are correct then rebuild the gateway to include the new configuration files in the installer.

By default the installer will put the files in the folder: C:\Program Files\Microsoft InnerEye Gateway, with the following folder structure (with the default GatewayModelRulesConfig):


├── Config
│   ├── GatewayModelRulesConfig
│   │   ├── GatewayModelRulesConfigPassThrough1.json
│   │   ├── GatewayModelRulesConfigPassThrough2.json
│   │   ├── GatewayModelRulesConfigPelvis.json
│   ├── GatewayProcessorConfig.json
│   ├── GatewayReceiveConfig.json
├── Microsoft InnerEye Gateway Processor
│   ├── Microsoft.InnerEye.Listener.Processor.exe
│   ├── log4net.config
│   ├── ... (all other dependencies)
├── Microsoft InnerEye Gateway Receiver
│   ├── Microsoft.InnerEye.Listener.Receiver.exe
│   ├── log4net.config
│   ├── ... (all other dependencies)

Both processor and receiver are installed as windows services that start automatically. The config files log4net.config control the logging for their respective service. They are copied from the source folders, e.g.

./Source/Microsoft.Gateway/Microsoft.InnerEye.Listener.Processor/log4net.config

By default they are both set to create a new log file each day and keep a total of 5 log files.

Architecture

The gateway runs as two applications or services configured with JSON files. The two applications communicate, and operate internally, using message queues built on Sqlite.

Sequence

To edit this, see: Rebuild Diagram.

Receiver Application

The first application is Microsoft.InnerEye.Listener.Receiver. This create a DICOM server that listens on an IP port for incoming DICOM communications and stores requests that are accepted on a message queue. This is configured in Receiver Configuration.

In detail, this:

  1. Starts a DICOM server that listens for incoming DICOM messages.

  2. On a DICOM Association Request, checks against the configured acceptable SOP classes and their transfer syntaxes.

  3. On a DICOM C-Store Request, stores the incoming DICOM image file to a subfolder of the RootDicomFolder. There will be one of these for each image slice.

  4. On a DICOM Association Release Request, puts a new message on the Upload message queue with details of the DICOM Association and location of the files.

Processor Application

The second application is Microsoft.InnerEye.Listener.Processor. This waits for messages on the Upload message queue from the Receiver Application. When a new message is received it: copies and de-identifies the received DICOM images, sends them to the InnerEye-Inference web service, waits and then downloads the resulting DICOM-RT file. This is then de-anonymised and sent on to the configured destination.

This is configured in Processor Configuration.

In detail, this starts 4 worker tasks: the Delete Service, Download Service, Push Service, and Upload Service, which communicate using the message queues: Delete, Download, Push, and Upload.

Upload Service

This service watches the Upload message queue for messages from the Receiver application that indicate a DICOM request has completed. When a new message is received it:

  1. Tries to find an application entity model configured with the matching CalledAET and CallingAET (Model Configuration).

  2. If there is a corresponding model and it is set to Model or ModelWithResultDryRun:

    a. Reads the received DICOM image files from the subfolder of the RootDicomFolder.

    b. Groups the DICOM image files by Study Instance UID, and then grouped by Series Instance UID.

    c. Compares each group with each channel config until a set of filtered DICOM image files matches the constraints in the channel config.

    d. Copies the image data and only the required DICOM tags to a set of new images, removing the remaining DICOM tags.

    e. Zips the images and POSTs them to the InnerEye-Inference web service.

    f. If the model ShouldReturnImage is set then copies all sent data to either the Results or DryRunModelWithResultFolder subfolders of the RootDicomFolder.

    g. Creates a new message on the Download queue for this web service request.

    h. Creates a new message on the Delete queue to delete the received DICOM image files.

  3. If there is a corresponding model and it is set to ModelDryRun:

    a. Reads the received DICOM image files from the subfolder of the RootDicomFolder.

    b. Removes the user-defined DICOM tags specified for de-identification.

    c. Saves the anonymised image files to the DryRunModelAnonymizedImage subfolder of the RootDicomFolder.

    d. Creates a new message on the Delete queue to delete the received image files.

  4. If there is no correspond model then:

    a. Creates a new message on the Delete queue to delete the received image files.

Download Service

This service watches the Download message queue for messages from the Upload Service that indicate a set of DICOM files have been sent to the InnerEye-Inference web service. When a new message is received it:

  1. Waits for the InnerEye-Inference web service to perform segmentation then downloads the resulting DICOM-RT file.

  2. De-anonymises the DICOM-RT file using data from the original received DICOM image files.

  3. Saves the de-anonymised file to either the Results or DryRunModelWithResultFolder subfolders of the RootDicomFolder.

  4. If this is not ModelWithResultDryRun then creates a new message on the Push message queue.

Push Service

This service watches the Push message queue for messages from the Download Service that indicate that a DICOM-RT file has been downloaded from the InnerEye-Inference web service. When a new message is received it:

  1. Tries to find an application entity model configured with the matching CalledAET and CallingAET (Model Configuration).

  2. Reads the de-anonymised DICOM-RT file from the Results subfolder of the RootDicomFolder.

  3. Sends them to the DICOM Destination as set in the application entity model.

  4. Creates a new message on the Delete queue to delete the received DICOM-RT file.

Delete Service

This service watches the Delete message queue for messages from the other services. If it receives a message it will delete the specified files or folders.

Anonymisation

The InnerEye Gateway allows users to define a set of identifiers that will be removed before being sent to the InnerEye-Inference web service. The set of identifier tags for removal are user-defined in GatewayProcessorConfig.json.

Users should ensure that the file represents the appropriate level of de-identification, including against any applicable organizational or local requirements.

The Gateway service processes and de-identifies the DICOM files using the procedure below.

Gateway DICOM Process

  1. The Receiver Application saves the incoming DICOM files directly to a subfolder of RootDicomFolder and passes a message to the Upload Service.

  2. The Upload Service loads the DICOM files and makes a copy of each file in memory (discarding the image data) to use as reference images for de-anonymisation later. The following tags are always kept, as well as any additional tags specified in GatewayProcessorConfig.json:

      // Patient module
  PatientID,
  PatientName,
  PatientBirthDate,
  PatientSex,

  // Study module
  StudyDate,
  StudyTime,
  ReferringPhysicianName,
  StudyID,
  AccessionNumber,
  StudyDescription

  3. The Upload Service loads the DICOM files again and makes a second copy of each file in memory, performing the following transformations to the DICOM tags:

    1. Tags specified in GatewayProcessorConfig.json are kept and transformed according to their specified anonymisation method (i.e "Keep", "Hash" or "Random").

    2. All other tags are discarded.

    3. The transformed, anonymised DICOM files are then zipped before sending to the InnerEye-Inference web service.

  4. Upon completion of the inference run, the Download Service downloads the DICOM-RT file and de-anonymises the segmented images by replacing their hashed, randomised and discarded tags with those saved in the reference image.

Anonymisation Testing

Anonymisation is tested extensively across multiple sets of tests. These can be found in the config tests file, the DICOM anonymisation tests file and the end-to-end tests file.

Configuration

Common Configuration

Both applications share some common configuration.

The InnerEye-Gateway repeatedly checks if its configuration files have been modified, to ensure that it can be updated without interrupting the DICOM processing. This is configured with ConfigurationServiceConfig. The algorithm behind this is that each ConfigurationRefreshDelaySeconds the application will check connectivity to the InnerEye-Inference service (for the Processor service) and check for changes to the configuration files. If ConfigCreationDateTime is different to the last loaded configuration file and ApplyConfigDateTime is past then the application will effectively restart with the new configuration.

{
  "ServiceSettings": {
    "RunAsConsole": boolean
  },
  << service specific configuration >>,
  "ConfigurationServiceConfig": {
    "ConfigCreationDateTime": date/time,
    "ApplyConfigDateTime": date/time,
    "ConfigurationRefreshDelaySeconds": number
  }
}

For example:

{
  "ServiceSettings": {
    "RunAsConsole": true
  },
  << service specific configuration >>,
  "ConfigurationServiceConfig": {
    "ConfigCreationDateTime": "2020-05-31T20:14:51",
    "ApplyConfigDateTime": "2020-05-31T20:14:51",
    "ConfigurationRefreshDelaySeconds": 60
  }
}

Where:

Processor Configuration

The processor application is configured by ./Source/Microsoft.Gateway/SampleConfigurations/GatewayProcessorConfig.json.

The structure of this configuration file is:

{
  "ServiceSettings": {
    "RunAsConsole": boolean
  },
  "ProcessorSettings": {
    "LicenseKeyEnvVar": string,
    "InferenceUri": string
  },
  "DequeueServiceConfig": {
    "MaximumQueueMessageAgeSeconds": number,
    "DeadLetterMoveFrequencySeconds": number
  },
  "DownloadServiceConfig": {
    "DownloadRetryTimespanInSeconds": number,
    "DownloadWaitTimeoutInSeconds": number
  },
  "ConfigurationServiceConfig": {
    "ConfigCreationDateTime": date/time,
    "ApplyConfigDateTime": date/time,
    "ConfigurationRefreshDelaySeconds": number
  },
  "AnonymisationSettings": {
    "DicomTagsAnonymisationConfig": {
        "string": [string],
      }
    ]
  }
}

For example:

{
  "ServiceSettings": {
    "RunAsConsole": true
  },
  "ProcessorSettings": {
    "LicenseKeyEnvVar": "MYINFERENCELICENSEKEY",
    "InferenceUri": "https://myinnereyeinference.azurewebsites.net"
  },
  "DequeueServiceConfig": {
    "MaximumQueueMessageAgeSeconds": 100,
    "DeadLetterMoveFrequencySeconds": 1
  },
  "DownloadServiceConfig": {
    "DownloadRetryTimespanInSeconds": 5,
    "DownloadWaitTimeoutInSeconds": 3600
  },
  "ConfigurationServiceConfig": {
    "ConfigCreationDateTime": "2020-05-31T20:14:51",
    "ApplyConfigDateTime": "2020-05-31T20:14:51",
    "ConfigurationRefreshDelaySeconds": 60
  },
  "AnonymisationSettings": {
    "DicomTagsAnonymisationConfig": {
      "Keep": [
        "PatientPosition",
        "Columns",
        "Rows",
        "PixelSpacing",
        "ImagePositionPatient",
        "ImageOrientationPatient",
        "SliceLocation",
        "Modality",
        "ModalityLUTSequence",
        "BodyPartExamined",
        "HighBit",
        "BitsStored",
        "BitsAllocated",
        "SamplesPerPixel",
        "PixelData",
        "PhotometricInterpretation",
        "PixelRepresentation",
        "RescaleIntercept",
        "RescaleSlope",
        "ImageType",
        "SOPClassUID",
        "RTReferencedStudySequence",
        "ReferencedROINumber",
        "ROIDisplayColor",
        "ContourSequence",
        "ROIContourSequence",
        "ReferencedSOPClassUID",
        "NumberOfContourPoints",
        "ContourData",
        "ContourGeometricType",
        "ContourImageSequence",
        "ObservationNumber",
        "RTReferencedSeriesSequence",
        "ReferencedFrameOfReferenceSequence",
        "ROINumber",
        "ROIName"
      ],
      "Hash": [
        "SeriesInstanceUID",
        "StudyInstanceUID",
        "SOPInstanceUID",
        "FrameOfReferenceUID",
        "ReferencedSOPInstanceUID",
        "RTROIObservationsSequence",
        "StructureSetLabel",
        "StructureSetName",
        "ReferencedFrameOfReferenceUID",
        "ROIGenerationAlgorithm",
        "StructureSetROISequence",
      ]
    }
  }
}
  }
}

Where:

Receiver Configuration

The receiver application is configured by ./Source/Microsoft.Gateway/SampleConfigurations/GatewayReceiveConfig.json and also by model configuration files in the folder ./Source/Microsoft.Gateway/SampleConfigurations/GatewayModelRulesConfig. The model configuration is explained below: Model Configuration.

The structure of GatewayReceiveConfig.json configuration file is:

{
  "ServiceSettings": {
    "RunAsConsole": boolean
  },
  "ReceiveServiceConfig": {
    "GatewayDicomEndPoint": {
      "Title": string,
      "Port": number,
      "Ip": string
    },
    "RootDicomFolder": string,
    "AcceptedSopClassesAndTransferSyntaxesUIDs": object of the form {
      string: array of strings,
      string: array of strings,
      string: array of strings,
      ...
    }
  },
  "ConfigurationServiceConfig": {
    "ConfigCreationDateTime": date/time,
    "ApplyConfigDateTime": date/time,
    "ConfigurationRefreshDelaySeconds": number
  }
}

For example:

{
  "ServiceSettings": {
    "RunAsConsole": true
  },
  "ReceiveServiceConfig": {
    "GatewayDicomEndPoint": {
      "Title": "GATEWAY",
      "Port": 111,
      "Ip": "localhost"
    },
    "RootDicomFolder": "C:\\InnerEyeGateway\\",
    "AcceptedSopClassesAndTransferSyntaxesUIDs": {
      "1.2.840.10008.1.1": [ "1.2.840.10008.1.2.1", "1.2.840.10008.1.2" ],
      "1.2.840.10008.5.1.4.1.1.481.3": [ "1.2.840.10008.1.2", "1.2.840.10008.1.2.1" ],
      "1.2.840.10008.5.1.4.1.1.2": [ "1.2.840.10008.1.2", "1.2.840.10008.1.2.1", "1.2.840.10008.1.2.4.57", "1.2.840.10008.1.2.4.70", "1.2.840.10008.1.2.4.80", "1.2.840.10008.1.2.5" ],
      "1.2.840.10008.5.1.4.1.1.4": [ "1.2.840.10008.1.2", "1.2.840.10008.1.2.1", "1.2.840.10008.1.2.4.57", "1.2.840.10008.1.2.4.70", "1.2.840.10008.1.2.4.80", "1.2.840.10008.1.2.5" ]
    }
  },
  "ConfigurationServiceConfig": {
    "ConfigCreationDateTime": "2018-07-25T20:14:51.539351Z",
    "ApplyConfigDateTime": "2018-07-25T20:14:51.539351Z",
    "ConfigurationRefreshDelaySeconds": 60
  }
}

Where:

Model Configuration

The application entity models are configured in the folder ./Source/Microsoft.Gateway/SampleConfigurations/GatewayModelRulesConfig. The configuration may be split across multiple JSON files, if desired, and the configuration will be merged. The structure of these files is that each must contain an array of the form:

[ array of application entity model config objects of the form:
  {
    "CallingAET": string,
    "CalledAET": string,
    "AETConfig": {
      "Config": {
        "AETConfigType": string, one of "Model", "ModelDryRun", or "ModelWithResultDryRun"
        "ModelsConfig": [ array of models config objects of the form:
          {
            "ModelId": string,
            "ChannelConstraints": [ array of channel constraints objects ],
            "TagReplacements": [ array of tag replacements objects ]
          }
        ]
      },
      "Destination": {
        "Title": string,
        "Port": number,
        "Ip": string
      },
      "ShouldReturnImage": boolean
    }
  }
]

For example:

[
  {
    "CallingAET": "RADIOMICS_APP",
    "CalledAET": "PassThroughModel",
    "AETConfig": {
      "Config": {
        "AETConfigType": "Model",
        "ModelsConfig": [
          {
            "ModelId": "PassThroughModel:3",
            "ChannelConstraints": [
              {
                "ChannelID": "ct",
                "ImageFilter": {
                  "Constraints": [
                    {
                      "RequirementLevel": "PresentNotEmpty",
                      "Constraint": {
                        "Function": {
                          "Order": "Equal",
                          "Value": {
                            "Value": "1.2.840.10008.5.1.4.1.1.2",
                            "ComparisonType": 0
                          },
                          "Ordinal": 0
                        },
                        "Index": {
                          "Group": 8,
                          "Element": 22
                        },
                        "discriminator": "UIDStringOrderConstraint"
                      },
                      "discriminator": "RequiredTagConstraint"
                    }
                  ],
                  "Op": "And",
                  "discriminator": "GroupConstraint"
                },
                "ChannelConstraints": {
                  "Constraints": [
                    {
                      "RequirementLevel": "PresentNotEmpty",
                      "Constraint": {
                        "Function": {
                          "Order": "Equal",
                          "Value": {
                            "Value": "1.2.840.10008.5.1.4.1.1.2",
                            "ComparisonType": 0
                          },
                          "Ordinal": 0
                        },
                        "Index": {
                          "Group": 8,
                          "Element": 22
                        },
                        "discriminator": "UIDStringOrderConstraint"
                      },
                      "discriminator": "RequiredTagConstraint"
                    }
                  ],
                  "Op": "And",
                  "discriminator": "GroupConstraint"
                },
                "MinChannelImages": 50,
                "MaxChannelImages": 1000
              }
            ],
            "TagReplacements": [
              {
                "Operation": "UpdateIfExists",
                "DicomTagIndex": {
                  "Group": 12294,
                  "Element": 2
                },
                "Value": "InnerEye"
              },
              {
                "Operation": "AppendIfExists",
                "DicomTagIndex": {
                  "Group": 12294,
                  "Element": 38
                },
                "Value": " NOT FOR CLINICAL USE"
              }
            ]
          }
        ]
      },
      "Destination": {
        "Title": "RADIOMICS_APP",
        "Port": 104,
        "Ip": "127.0.0.1"
      },
      "ShouldReturnImage": false
    }
  }
]

Where:

ModelsConfig

Each model config has the following structure:

{
  "ModelId": string,
  "ChannelConstraints": [ array of objects of the form
    {
      "ChannelID": string,
      "ImageFilter": {
        "Constraints": [ array of constraints ],
        "Op": string, one of "And" or "Or",
        "discriminator": "GroupConstraint"
      },
      "ChannelConstraints": {
        "Constraints": [ array of constraints ],
        "Op": string, one of "And" or "Or",
        "discriminator": "GroupConstraint"
      },
      "MinChannelImages": number,
      "MaxChannelImages": number
    }
  ],
  "TagReplacements": [ array of objects of the form:
    {
      "Operation": string, one of "UpdateIfExists" or "AppendIfExists",
      "DicomTagIndex": {
        "Group": number,
        "Element": number
      },
      "Value": string
    }
  ]
}

For example:

{
  "ModelId": "PassThroughModel:3",
  "ChannelConstraints": [
    {
      "ChannelID": "ct",
      "ImageFilter": {
        "Constraints": [
          {
            "RequirementLevel": "PresentNotEmpty",
            "Constraint": {
              "Function": {
                "Order": "Equal",
                "Value": {
                  "Value": "1.2.840.10008.5.1.4.1.1.2",
                  "ComparisonType": 0
                },
                "Ordinal": 0
              },
              "Index": {
                "Group": 8,
                "Element": 22
              },
              "discriminator": "UIDStringOrderConstraint"
            },
            "discriminator": "RequiredTagConstraint"
          }
        ],
        "Op": "And",
        "discriminator": "GroupConstraint"
      },
      "ChannelConstraints": {
        "Constraints": [
          {
            "RequirementLevel": "PresentNotEmpty",
            "Constraint": {
              "Function": {
                "Order": "Equal",
                "Value": {
                  "Value": "1.2.840.10008.5.1.4.1.1.2",
                  "ComparisonType": 0
                },
                "Ordinal": 0
              },
              "Index": {
                "Group": 8,
                "Element": 22
              },
              "discriminator": "UIDStringOrderConstraint"
            },
            "discriminator": "RequiredTagConstraint"
          }
        ],
        "Op": "And",
        "discriminator": "GroupConstraint"
      },
      "MinChannelImages": 50,
      "MaxChannelImages": 1000
    }
  ],
  "TagReplacements": [
    {
      "Operation": "UpdateIfExists",
      "DicomTagIndex": {
        "Group": 12294,
        "Element": 2
      },
      "Value": "InnerEye"
    },
    {
      "Operation": "AppendIfExists",
      "DicomTagIndex": {
        "Group": 12294,
        "Element": 38
      },
      "Value": " NOT FOR CLINICAL USE"
    }
  ]
}

Where:

ChannelConstraint

Each channel constraint has the form:

{
  "ChannelID": string,
  "ImageFilter": {
    "Constraints": [ array of constraints ],
    "Op": string, one of "And" or "Or",
    "discriminator": "GroupConstraint"
  },
  "ChannelConstraints": {
    "Constraints": [ array of constraints ],
    "Op": string, one of "And" or "Or",
    "discriminator": "GroupConstraint"
  },
  "MinChannelImages": number,
  "MaxChannelImages": number
}

Where:

DicomConstraint

There are many types of DicomConstraint. So they can be identified when loading the JSON they are all of the form:

{
  << constraint specific data >>,
  "discriminator": string
}

Where:

These are split into two groups. The first group contains GroupConstraint and RequiredTagConstraint and are sort of container objects. The second group are all instances of DicomTagConstraint which specify a DICOM tag and a constraint to be applied.

GroupConstraint

This constraint acts as a container for a set of other constraints that must either all pass, or at least one of them must pass.

{
  "Constraints": [ array of constraints ],
  "Op": string, one of "And" or "Or",
  "discriminator": "GroupConstraint"
}

Where:

RequiredTagConstraint

This constraint acts as a container for a tag constraint and a requirement on the tag.

{
  "RequirementLevel": string, one of "PresentNotEmpty", "PresentCanBeEmpty", or "Optional",
  "Constraint": a dicom tag constraint object,
  "discriminator": "RequiredTagConstraint"
}

For example:

{
  "RequirementLevel": "PresentNotEmpty",
  "Constraint": {
    "Function": {
      "Order": "Equal",
      "Value": {
        "Value": "1.2.840.10008.5.1.4.1.1.2",
        "ComparisonType": 0
      },
      "Ordinal": 0
    },
    "Index": {
      "Group": 8,
      "Element": 22
    },
    "discriminator": "UIDStringOrderConstraint"
  },
  "discriminator": "RequiredTagConstraint"
}

Where:

DicomTagConstraint

This group of DICOM constraints operate on a DICOM tag. They are all of the form:

{
  << tag constraint specific data >>,
  "Index": {
    "Group": number,
    "Element": number
  },
  "discriminator": string
}

Where:

GroupTagConstraint

This acts as a container for a set of constraints and a DICOM tag that the constraints apply to.

{
  "Group": group constraint object,
  "Index": {
    "Group": number,
    "Element": number
  },
  "discriminator": "GroupTagConstraint"
}

Where:

StringContainsConstraint

This constraint tests that a DICOM tag contains a given value.

{
  "Match": string,
  "Ordinal": number,
  "Index": {
    "Group": number,
    "Element": number
  },
  "discriminator": "StringContainsConstraint"
}

For example:

{
  "Match": "AXIAL",
  "Ordinal": -1,
  "Index": {
    "Group": 8,
    "Element": 8
  },
  "discriminator": "StringContainsConstraint"
}

Where:

RegexConstraint

This constraint contains a regular expression and a tag to test against.

{
  "Expression": string, a regular expression, suitable for the Regex constructor from .NET System.Text.RegularExpressions,
  "Options": number, one of the RegexOptions from .NET System.Text.RegularExpressions,
  "Ordinal": number,
  "Index": {
    "Group": number,
    "Element": number
  },
  "discriminator": "RegexConstraint"
}

Where:

OrderedDateTimeConstraint, OrderedDoubleConstraint, OrderedIntConstraint, OrderedStringConstraint

These contain an ordering function and a DICOM tag that the ordering function applies to.

{
  "Function": {
    "Order": string, one of "Never", "LessThan", "Equal", "LessThanOrEqual", "GreaterThan", "NotEqual", "Always",
    "Value": object, one of date/time, number, or for strings {
      "Value": string,
      "ComparisonType": number, 0 for case sensitive string comparisons, 1 for case insensitive.
    },
    "Ordinal": 0
  },
  "Index": {
    "Group": number,
    "Element": number
  },
  "discriminator": string, one of "OrderedDateTimeConstraint", "OrderedDoubleConstraint", "OrderedIntConstraint", "OrderedStringConstraint"
}

For example:

{
  "Function": {
    "Order": "Equal",
    "Value": {
      "Value": "HEAD",
      "ComparisonType": 0
    },
    "Ordinal": 0
  },
  "Index": {
    "Group": 24,
    "Element": 21
  },
  "discriminator": "OrderedStringConstraint"
}

Where:

UIDStringOrderConstraint

This is a variant of the OrderedDateTimeConstraint etc, but contains a constraint for a DICOM UID tag.

{
  "Function": {
    "Order": string, order as above,
    "Value": {
      "Value": string,
      "ComparisonType": number, as above
    },
    "Ordinal": number
  },
  "Index": {
    "Group": number,
    "Element": number
  },
  "discriminator": "UIDStringOrderConstraint"
}

For example:

{
  "Function": {
    "Order": "Equal",
    "Value": {
      "Value": "1.2.840.10008.5.1.4.1.1.2",
      "ComparisonType": 0
    },
    "Ordinal": 0
  },
  "Index": {
    "Group": 8,
    "Element": 22
  },
  "discriminator": "UIDStringOrderConstraint"
}

Where the Function and Index are as above, except that the constraint test is applied to a DICOM UID tag.

TimeOrderConstraint

This is a variant of the OrderedDateTimeConstraint etc, but for a tag of TM value representation.

{
  "Function": {
    "Order": string, order as above,
    "Value": number, a TimeSpan in .NET Timespan serialization format,
    "Ordinal": number
  },
  "Index": {
    "Group": number,
    "Element": number
  },
  "discriminator": "TimeOrderConstraint"
}

For example:

{
  "Function": {
    "Order": "GreaterThanOrEqual",
    "Value": "16:05:42.7380000",
    "Ordinal": 0
  },
  "Index": {
    "Group": 25447,
    "Element": 60116
  },
  "discriminator": "TimeOrderConstraint"
}

Where the Function and Index are as above, except that the constraint test is applied to a DICOM tag TimeOfDay property.

OWASP

All communications between a deployed Inference app service and the InnerEye Gateway are compliant with OWASP 3.0.

Licensing

MIT License

You are responsible for the performance, the necessary testing, and if needed any regulatory clearance for any of the models produced by this toolbox.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com .

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct . For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct