TY - JOUR A1 - Lapenna, Michela A1 - Tsamos, Athanasios A1 - Faglioni, Francesco A1 - Fioresi, Rita A1 - Zanchetta, Ferdinando A1 - Bruno, Giovanni T1 - Geometric deep learning for enhanced quantitative analysis of microstructures in X-ray computed tomography data JF - Discover Applied Sciences N2 - Quantitative microstructural analysis of XCT 3D images is key for quality assurance of materials and components. In this paper we implement a Graph Convolutional Neural Network (GCNN) architecture to segment a complex Al-Si Metal Matrix composite XCT volume (3D image). We train the model on a synthetic dataset and we assess its performance on both synthetic and experimental, manually-labeled, datasets. Our simple GCNN shows a comparable performance, measured via the Dice score, to more standard machine learning methods, but uses a greatly reduced number of parameters (less than 1/10 of parameters), features low training time, and needs little hardware resources. Our GCNN thus achieves a cost-effective reliable segmentation. KW - Geometric deep learning KW - Segmentation KW - Microstructure KW - X-ray computed tomography KW - Al–Si metal matrix composites PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602517 DO - https://doi.org/10.1007/s42452-024-05985-0 SN - 3004-9261 VL - 6 SP - 1 EP - 9 AN - OPUS4-60251 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -