Data from: 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 permissible license, and can be adapted and re-used in other domains. Here you find the data resulting from this study.

纤维增强陶瓷基复合材料（fiber-reinforced ceramic-matrix composites）是一类耐高温的先进复合材料，可应用于航空航天工程领域。对这类材料的分析依赖于对其内嵌纤维的检测，通常采用半监督（semi-supervised）技术来分离纤维束内的单根纤维。本研究提出了一套开源计算流程，用于对非原位X射线计算机断层扫描（ex-situ X-ray computed tomography）纤维束样本中的纤维进行检测。为实现此类样本中纤维的分离，我们测试了四种不同架构的全卷积神经网络（fully convolutional neural networks）。将我们的神经网络方法与半监督（semi-supervised）方法进行对比后，得到的戴斯系数（Dice coefficient）与马修斯相关系数（Matthews coefficient）均高于92.28 ± 9.65%，最高可达98.42 ± 0.03%，表明该神经网络的检测结果与人工标注监督下的结果高度接近；在部分场景中，该模型还能分离出人工审核算法未能识别的纤维。本项目开发的软件为开源软件，采用宽松许可协议发布，可适配并复用至其他领域。本文附带本研究产生的全部相关数据集。