
Proceedings Paper
Building towards a universal neural network to segment large materials science imaging datasetsFormat | Member Price | Non-Member Price |
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Paper Abstract
Segmentation of large images can be one of the most time-consuming steps in the analysis of materials science datasets. Convolutional neural networks (NNs) have been shown to reduce segmentation time compared to manual techniques, but training a new NN is often required for each dataset. We show that simply combining NN training datasets does not necessarily lead to a NN capable of segmenting multiple types of images. In the present study, we first show that SegNet-based neural networks (NNs) can be trained to accurately segment Al-Zn x-ray computed tomography and Pb-Sn serial sectioning images. Applying the Al-Zn NN to the Pb-Sn test image led to misclassified smudges as dendrites, and misclassified speckles as background. Applying the Pb-Sn NN to the Al-Zn test image was unsuccessful, likely because the Al-Zn dendrites had a higher luminance than the Pb-Sn dendrites. The Mix NN (trained using the combined Al-Zn and Pb-Sn datasets) was better at segmenting the Pb-Sn test image than the Al-Zn test image. This is likely because the Pb-Sn training dataset contained ~4.5 times as many dendrite pixels as the Al-Zn training dataset, thus the Mix NN was over-tuned to identify Pb-Sn dendrites. Simply combining the training datasets was overall detrimental to NN performance, but assigning different classes to the Al-Zn and Pb-Sn dendrites may lead to enhanced performance in the future. These findings serve as guidelines in the quest to develop a universal NN for segmentation of large materials science datasets.
Paper Details
Date Published: 10 September 2019
PDF: 6 pages
Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131G (10 September 2019); doi: 10.1117/12.2525290
Published in SPIE Proceedings Vol. 11113:
Developments in X-Ray Tomography XII
Bert Müller; Ge Wang, Editor(s)
PDF: 6 pages
Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131G (10 September 2019); doi: 10.1117/12.2525290
Show Author Affiliations
Tiberiu Stan, Northwestern Univ. (United States)
Zachary T. Thompson, Northwestern Univ. (United States)
Zachary T. Thompson, Northwestern Univ. (United States)
Peter W. Voorhees, Northwestern Univ. (United States)
Published in SPIE Proceedings Vol. 11113:
Developments in X-Ray Tomography XII
Bert Müller; Ge Wang, Editor(s)
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