Sheiati, ShohrehShohrehSheiatiBehboodi, SanazSanazBehboodiRanjbar, NavidNavidRanjbar2022-09-012022-09-012022https://resolver.sub.uni-goettingen.de/purl?gro-2/113465Segmentation of backscattered electron (BSE) images of cementitious materials is often used to quantify different microstructural features for the sake of performance estimation at macro-scale levels. However, the heterogeneous nature of cementitious matrices compounds with varying imaging conditions can lead the conventional segmentation methods to a processing bottleneck for largescale experiments. To overcome these challenges, in this study, we evaluate the potential of deep autoencoder convolutional networks, specifically SegNet, for automatic segmentation of fly ash-based geopolymer images. We present the SegNet power in achieving a comparable accuracy to the human performance even with a few BSE images in the model’s training. The SegNet demonstrates magnification independent training that adapts itself with both seen and unseen magnification levels. A comparative study shows that SegNet outperforms the Gaussian method on uncontrolled imaging conditions such as background brightness levels. In addition, we demonstrate the self-learning capability of SegNet in poorly annotated areas.enCC BY 4.0https://www.elsevier.com/tdm/userlicense/1.0/journal_article10.1016/j.eswa.2022.117846S0957417422011022