Use Case - Radiology

This example covers the annotation use-case for radiology using the sample MONAILabel App - radiology. MONAI Label server currently supports the annotation of local datasets via 3DSlicer, such as unlabeled images residing on disk, and remote data residing on DICOMweb-enabled PACS systems via both 3DSlicer and OHIF.

DeepEdit Annotation with 3DSlicer

Deploy MONAI Label Server

On the local machine follow the commands listed below to install MONAI Label, download a sample application (Radiology), download a sample dataset (MSD heart MRI), and deploy the sample app and standard dataset on the MONAI Label server.

# install MONAI Label
pip install monailabel

# download Radiology sample app to local directory
monailabel apps --name radiology --download --output .

# download Task 2 MSD dataset
monailabel datasets --download --name Task09_Spleen --output .

# start the Radiology app in MONAI label server
# and start annotating the downloaded images using deepedit model
monailabel start_server --app radiology --studies Task09_Spleen/imagesTr --conf models deepedit

Install MONAI Label Plugin in 3DSlicer

Install 3DSlicer Preview Version with in-built MONAI Label plugin

  • Download and Install 3D Slicer version 5.0 or later.

  • Start 3DSlicer

  • On the menu bar navigate View -> Extension Manager -> Active Learning -> MONAI Label

    3DSlicer Extensions Manager
  • Install MONAI Label plugin (click “Install”)

  • Restart 3D Slicer (click “Restart” in the same dialog box)

To add the MONAI Label icon shortcut on the 3DSlicer toolbar

  • Navigate Edit -> Application Settings

  • Under the Modules panel drag MONAI Label into Favorite Modules

    MONAI Label Favorite Module
  • Restart 3DSlicer

  • Look for the MONAI Label module icon MLIcon in the 3DSlicer toolbar

DeepEdit Annotation in 3DSlicer

To connect 3DSlicer to MONAI Label Server

  • Click on the MONAI Label module icon

  • Click the refresh icon near next to the MONAI Label server input field

    • If the MONAI Label server is running locally to 3DSlicer you do not need to enter the server address

    MONAI Label Refresh Button

To annotate the downloaded heart MR images using DeepEdit

  • Click Next Sample under Strategy to retrieve an image from the heart dataset along with a segmentation result generated by DeepEdit

    Next Sample
  • We can now start making adjustments to the initial segmentation provided by adding foreground and background points using the tools in the SmartEdit section

    • For example, click on the foreground button and start clicking on the image panel to start seeing how foreground points affect the segmentation

    MONAI Label SmartEdit
  • Once we are satisfied with the segmentation we can Submit Label

    MONAI Label Submit Label Button
  • We repeat the last four steps until our dataset is annotated

Annotating a Custom Dataset

To annotate a custom dataset using DeepEdit, we can download the DeepEdit app as above, however, the dataset directory need not be populated. Follow the commands below to setup custom dataset annotation using the empty local directory my_dataset as the image and label storage location.

# install MONAI Label
pip install monailabel

# download DeepEdit sample app to local directory
monailabel apps --name deepedit --download --output .

# create an empty folder for the custom dataset
mkdir my_dataset

# start the DeepEdit app in MONAI label server
# on the empty dataset folder
monailabel start_server --app radiology --studies my_dataset --conf models deepedit

We can follow the instructions in the previous section to install and connect 3DSlicer to MONAI Label Server, however, in this scenario we will instead load a file into MONAI Label Server through 3DSlicer.

  • Load an image file by navigating the menu bar File -> Add Data

  • Click the referesh button under MONAI Label Server to connect to the server

  • Click the Upload Volume button to upload the new image onto the server

    MONAI Label Upload Image
  • Now, all DeepEdit functions should be available to use and we use foreground and background clicks

DeepEdit Annotation Using OHIF

As of version 0.2.0, MONAI Label server supports connectivity to a remote DICOM server via DICOMweb. All we need when starting MONAI Label server is to specify the URL of the DICOMweb service in the studies argument (and optionally the username and password for DICOM servers that require them).

If you do not have a DICOM server available for usage but would like to set one up please follow the instructions in the next section, otherwise skip to DeepEdit Annotation in OHIF.

Setup Development DICOM Server

Orthanc is an open-source lightweight DICOM server for medical imaging. To setup an instance of Orthanc on your machine of choice follow the guides below.


# Install orthanc and dicomweb plugin
sudo apt-get install orthanc orthanc-dicomweb -y

# stop the existing Orthanc instance if there is one
sudo service orthanc stop

# setup and upgrade Orthanc libraries
sudo wget https://lsb.orthanc-server.com/orthanc/1.9.7/Orthanc --output-document /usr/sbin/Orthanc
sudo rm -f /usr/share/orthanc/plugins/*.so

sudo wget https://lsb.orthanc-server.com/orthanc/1.9.7/libServeFolders.so --output-document /usr/share/orthanc/plugins/libServeFolders.so
sudo wget https://lsb.orthanc-server.com/orthanc/1.9.7/libModalityWorklists.so --output-document /usr/share/orthanc/plugins/libModalityWorklists.so
sudo wget https://lsb.orthanc-server.com/plugin-dicom-web/1.6/libOrthancDicomWeb.so --output-document /usr/share/orthanc/plugins/libOrthancDicomWeb.so

# start
sudo service orthanc restart


Download and Install Orthanc from https://www.orthanc-server.com/download.php.

The Orthanc DICOM server on the chosen machine. You can check if the server is running by navigating to or using the remote machine’s address and entering the username/password orthanc/orthanc.

The DICOMweb service for Orthanc runs on by default. You can check if the DICOMweb endpoint is active by issuing the following command

curl -X GET -v

You may encounter a 401 Unauthorized response when username and password are required.


When trying to access Orthanc remotely, please make sure you update the default configuration to allow for remote connections, by opening /etc/orthanc/orthanc.json and setting RemoteAccessAllowed to true.

Adding Data to Development DICOM Server

If you do not have access to DICOM data to upload to the DICOM server you can convert from the NIFTI available via MONAI Label.

# install MONAI Label (if you have not already)
pip install monailabel

# Install `plastimatch` NIFTI to DICOM converter
sudo apt-get install plastimatch -y

# download Task 2 MSD dataset
monailabel datasets --download --name Task09_Spleen --output .

# convert one of the NIFTI images to DICOM
plastimatch convert --patient-id patient1 --input Task09_Spleen/imagesTs/spleen_10.nii.gz --output-dicom dicom_output

Now, we can upload the DICOM series in dicom_output using the upload link in Orthanc.

You may use plastimatch to convert more NIFTI files to DICOM to keep populating the development DICOM server.

DeepEdit Annotation in OHIF

We follow a very similar set of commands as in Deploy MONAI Label Server, however, we use the DICOMweb endpoint of our DICOM server, which based on the last section is http://locahost:8042/dicom-web.

# install MONAI Label (if you have not already)
pip install monailabel

# download DeepEdit sample app to local directory
monailabel apps --name radiology --download --output .

# start the DeepEdit app in MONAI label server
# and start annotating images in our DICOM server
monailabel start_server --app radiology --studies http://locahost:8042/dicom-web --conf models deepedit --username orthanc --password orthanc

At this point OHIF can be used to annotate the data in the DICOM server via the MONAI Label server /ohif endpoint (e.g. via


Here, user may also perform annotation using 3DSlicer by following the same instructions as in section DeepEdit Annotation in 3DSlicer.