Image Segmentation using U-Net Architecture for Powder X-ray Diffraction
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Howard Yanxon Eric Roberts Hannah Parraga James Weng Wenqian Xu Uta Ruett Alexander Hexemer Petrus Zwart Nickolas Schwarz
Scientific researchers frequently use the in situ synchrotron high-energy
powder X-ray diffraction (XRD) technique to examine the crystallographic
structures of materials in functional devices such as rechargeable battery
materials. We propose a method for identifying artifacts in experimental XRD
images. The proposed method uses deep learning convolutional neural network
architectures, such as tunable U-Nets to identify the artifacts. In particular,
the predicted artifacts are evaluated against the corresponding ground truth
(manually implemented) using the overall true positive rate or recall. The
result demonstrates that the U-Nets can consistently produce great recall
performance at 92.4% on the test dataset, which is not included in the
training, with a 34% reduction in average false positives in comparison to the
conventional method. The U-Nets also reduce the time required to identify and
separate artifacts by more than 50%. Furthermore, the exclusion of the
artifacts shows major changes in the integrated 1D XRD pattern, enhancing
further analysis of the post-processing XRD data.