A reusable neural network pipeline for unidirectional fiber segmentation

Alexandre Fioravante de Siqueira, Daniela M. UshizimaStéfan J. van der Walt

Nature Scientific Data
February 2, 2022

Abstract: Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al. To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

Featured Fellows

Alex de Siqueira

Assistant Project Scientist, Data Science Outreach Lead

Daniela Ushizima

Computational Research Division, CAMERA, LBNL
Research Affiliate

Stéfan van der Walt

Senior Research Data Scientist