ArticleScientific data2022
A reusable neural network pipeline for unidirectional fiber segmentation.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures.Journal of imaging · 2025Article
- Lithium Metal Battery Quality Control via Transformer-CNN Segmentation.Journal of imaging · 2023Article
- Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.IS&T International Symposium on Electronic Imaging · 2023Article
- A reusable neural network pipeline for unidirectional fiber segmentation.Scientific data · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.