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