TY - JOUR A1 - Lapenna, M. A1 - Tsamos, Athanasios A1 - Faglion, F. A1 - Fioresi, R. A1 - Zanchetta, F. A1 - Bruno, Giovanni T1 - Vision GNN (ViG) architecture for a fine‑tuned segmentation of a complex Al–Si metal matrix composite XCT volume N2 - In this paper, we implement a vision graph neural network (ViG) architecture to segment microstructures in X-ray computed tomography 3D data. Our ViG architecture is first trained on a synthetic augmented dataset, and then fine-tuned on experimental data to obtain an improved segmentation. Successively, we assess the accuracy of the segmentation on manually-labeled experimental slices. We exemplarily use the approach on a complex microstructure: a metal matrix composite, reinforced with two ceramic phases, intermetallic inclusions and a silicon network, in order to show the generality of our method. ViG model proves to be more efficient than U-Nets in adapting to new data when fine-tuned on a small portion of the experimental data. The fine-tuned ViG shows comparable performance to U-Nets, while largely reducing the number of trainable parameters, with the potential of greater adaptability and efficiency. KW - X-ray Computed tomography KW - Machine Learning KW - Virtual XCT KW - Segmentation KW - Composites PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630593 DO - https://doi.org/10.1007/s10853-025-10834-5 SN - 1573-4803 VL - 60 SP - 6907 EP - 6921 PB - Springer AN - OPUS4-63059 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -