First, we need to set up the viewer and server environment. To run the server, we need to start our environment by opening the System Console and starting our docker container:
docker run --gpus all --rm -ti --ipc=host --net=host -v /home/nvidia/datasets:/opt/monai/datasets projectmonai/monailabel:latest bash
Skip this step if you’ve already started the container for other app. There’s no need to start a new container if you already started one for another app. Stop the previous server by pressing Ctrl+C and go to the next step.
The System Console can be found on the top left on this page:
After starting the docker container, the next step is to download the radiology app and start the server using a sample dataset (Task09_Spleen).
Download the radiology app:
monailabel apps --download --name radiology --output apps
Start the server using radiology app with the segmentation_spleen model enabled:
monailabel start_server --app apps/radiology --studies datasets/Task09_Spleen/unlabeled_imgs --conf models segmentation_spleen
In this case, we assume the dataset is completely unlabeled and that we want to use the spleen pretrained model to segment the volumes.