“…In addition, the annotation itself is error prone due to the inevitable loss of attention of the operator in charge of annotating the images. The main originality of our approach is that we entirely trained the network on a series of synthetic images generated with morphological models (Figliuzzi, 2019;Figliuzzi et al, 2021;Jeulin, 2021;Stoyan et al, 2013), commonly used to simulate micro-structures in materials engineering (Bortolussi et al, 2018;Figliuzzi et al, 2016), rather than on images of real experiments. With our approach of artificial synthesis of the ground truth, the positions and sizes of the particles are known by construction, which allows us to get rid of the difficulty of obtaining a reliable learning database 1 .…”