This MONAI Label App is based on a non-interactive model such as the standard UNet architecture or any other architecture the researcher wants to use. This App follows the same structure as the DeepGrow and DeepEdit Apps. The most important difference between this type of Apps and the interactive one is that it doesn't simulate clicks representing the foreground and background.

In order to create a single label segmentation model, users can start by running the following command:

monailabel apps --download --name radiology --output apps

to create a non-interactive segmentation model. Then, change the ROI size, image spacing, and other transforms in the train_post_processing method if necessary. Otherwise, just organise the dataset as explained next.

This example shows the use of nifty files (compressed and uncompressed) with a single modality for segmentation. As with all the Apps, the user has two options to organise the dataset: (1) upload images and labels through the Slicer module, or (2) organising the dataset on the server-side. For the latter, the researcher should make sure the folder contains the images and a subfolder called labels/final containing the labels with exactly the same image name. As it is shown here: https://youtu.be/o8HipCgSZIw?t=748

When modifying your own App, make sure you are properly addressing these points:

• Is the image spacing properly defined?
• Is the intensity normalization customised to the task?
• Should you crop the image? Define the cropping (i.e. foreground cropping, central cropping, etc) if possible for better performance
• Don't forget to define the inverse transforms method in the infer script

Remember that all MONAI Label Apps have access to the heuristic planner. Check Heuristic Planner for more details.