To deal with microscopy datasets containing a large number of objects per image, we implemented partial annotation. It's not efficient to annotate all objects of a single image for ML training; it's more beneficial to include some objects from different images that span multiple experiments. This approach allows for training a more robust segmentation model, one that includes objects of all sizes, shapes, and colors that are expected to be segmented.
To distinguish between non-annotated regions and regions that don't contain any class that should be segmented, we introduced the background class. The background is a special class that tells the neural network that the pixels it covers aren't occupied by any of the classes the model should recognize. If there are regions you don't want to be segmented, it's essential to provide background annotations. Non-annotated pixels are completely ignored during model training, allowing you to partially annotate the dataset.