Glass fiber-reinforced polyamide 66 3D X-ray computed tomography dataset for deep learning segmentation

Stack of 2D gray images of glass fiber-reinforced polyamide 66 (GF-PA66) 3D X-ray Computed Tomography (XCT) specimen.

Usage: 2D/3D image segmentation
Format: HDF5

Libraries to read HDF5 files:

1) silx: https://github.com/silx-kit/silx

2) h5py: https://www.h5py.org/

3) pymicro: https://github.com/heprom/pymicro

Trained models to segment this dataset: https://doi.org/10.5281/zenodo.4601560 

Please cite us as

@ARTICLE{10.3389/fmats.2021.761229,
AUTHOR={Bertoldo, João P. C. and Decencière, Etienne and Ryckelynck, David and Proudhon, Henry},   
TITLE={A Modular U-Net for Automated Segmentation of X-Ray Tomography Images in Composite Materials},      
JOURNAL={Frontiers in Materials},      
VOLUME={8},      
YEAR={2021},      
URL={https://www.frontiersin.org/article/10.3389/fmats.2021.761229},       
DOI={10.3389/fmats.2021.761229},      
ISSN={2296-8016},     
}