DOI: 10.6078/d1069r
title: Data from: A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks

description: 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.
description: We implemented four architectures — two dimensional U-net and
 Tiramisu, and their three-dimensional versions — to process images of
 fiber beds provided by Larson et al. We used supervised algorithms: they
 rely on labeled data to learn what are the regions of interest — in our
 case, fibers within microtomographies of fiber beds. All CNN algorithms
 were implemented using TensorFlow and Keras on a computer with two Intel
 Xeon Gold processors 6134 and two Nvidia GeForce RTX 2080 graphical
 processing units. Each GPU has 10 GB of RAM. To train the neural networks
 on how to recognize the fibers, we used slices from two different samples:
 “232p3 wet” and “232p3 cured”, registered according to the wet sample.
 Larson et al. provided the fiber segmentation for these samples, which we
 used as labels in the training. The training and validation procedures
 processed 350 and 149 images from each sample, respectively; a total of
 998 images. Each image from the original samples have width and height
 size of 2560 × 2560 pixels. To feed the two-dimensional networks, we
 padded the images with 16 pixels, of value zero, in each dimension. Then,
 each image was cut into tiles of size 288 × 288, each 256 pixels, creating
 an overlap of 32 pixels. These overlapping regions, which are again
 removed after processing, avoid artifacts on the borders of processed
 tiles. Therefore, each input slice generated 100 images with 288 × 288
 pixels, in a total of 50,000 images for the training set, and 10,000 for
 the validation set.
description: We used twelve different datasets from Larson et al (2019) in our
 study. We kept the same folder identifiers used in their original data,
 for fast cross-reference. The filenames for each processed sample follow
 the structure `&lt;NETWORK&gt;-&lt;Larson's sample
 folder&gt;.zip`, where `&lt;NETWORK&gt;` can be `tiramisu`,
 `tiramisu_3d`, `unet`, `unet_3d`. For example, results for the sample
 232p3, wet, obtained with the 2D U-net network are given in the file
 `unet-rec20160318_191511_232p3_2cm_cont__4097im_1500ms_ML17keV_6.h5.zip`. The file `coefficients.zip` contains: 1. the training coefficients for each network, where filenames follow the structure `larson_&lt;NETWORK&gt;.hdf5`; 2. accuracy, loss, validation accuracy and validation loss we obtained during our training process; 3. filenames follow the structure `larson_&lt;NETWORK&gt;.hdf5-&lt;MEASURE&gt;.csv`, where `&lt;MEASURE&gt;` can be `accuracy`, `loss`, `val_accuracy`, `val_loss`, for accuracy, loss, validation accuracy and validation loss, respectively; 4. output of the training and prediction steps in our study, where filenames follow the structure `output.train_&lt;NETWORK&gt;.txt` and `output.predict_&lt;NETWORK&gt;.txt` for the training and prediction processes, respectively.