Vision GNN (ViG) architecture for a fine-tuned segmentation of a complex Al–Si metal matrix composite XCT volume

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.