A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks

Fiber-reinforced ceramic-matrix composites are advanced materials resistant to high temperatures, with application to aerospace engineering. Their analysis depends on the detection of embedded fibers, with semi-supervised techniques usually employed to separate fibers within the fiber beds. Here we present an open computational pipeline to detect fibers in ex-situ X-ray computed tomography fiber beds. To separate the fibers in these samples, we tested four different architectures of fully convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients greater than 92.28 ± 9.65%, reaching up to 98.42 ± 0.03%, showing that the network results are close to the human-supervised ones in these fiber beds, in some cases separating fibers that human-curated algorithms could not find. The software we generated in this project is open source, released under a permissive ∗alex.desiqueira@igdore.org †dushizima@lbl.gov ‡stefanv@berkeley.edu 1 ar X iv :2 10 1. 04 82 3v 2 [ ee ss .I V ] 1 5 Ja n 20 21 license, and can be freely adapted and re-used in other domains. All data and instructions on how to download and use it are also available.