Geometric deep learning for enhanced quantitative analysis of microstructures in X-ray computed tomography data

Author:

Lapenna M.,Tsamos A.,Faglioni F.,Fioresi R.,Zanchetta F.,Bruno G.

Abstract

AbstractQuantitative 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.

Funder

HORIZON EUROPE Marie Sklodowska-Curie Actions

European Cooperation in Science and Technology

Bundesanstalt für Materialforschung und -prüfung (BAM)

Publisher

Springer Science and Business Media LLC

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