This task consists of manually drawing the shape of the object over a set of representative images (2D or 3D). The reference objects should describe all their possible variation within the reference samples. The annotations are then used to create a binary masked image (Ground-truth). Both the annotations and the related binary masks are used afterward by the training task to build the Neuronal Network (training).

The annotation task is a manual activity and therefore requires a lot of time to be performed. The correct number of annotations must be estimated in advance in order to get reliable training results. During the training, the annotations can be increased if required. The annotations range starts from a minimum number of 200 samples, spread over 10 - 20 different images, up to a thousand (or even tens of thousands) in the more complex cases.

In order to increase the number of samples without the need to acquire new images, it is also possible to re-use the already existing images after applying some operations like rotations, random flips, or intensity shifts of the original.