Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images
| Autor: | Colin Ophus, Robbie Sadre, Gunther H. Weber, Anastasiia Butko |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Source code Computer science media_common.quotation_subject cs.LG FOS: Physical sciences Image processing Bioengineering 02 engineering and technology Machine Learning (cs.LG) law.invention 03 medical and health sciences monolayer graphene law Segmentation high-resolution transmission electron microscopy automated segmentation High-resolution transmission electron microscopy Instrumentation defects 030304 developmental biology media_common 0303 health sciences Condensed Matter - Materials Science Microscopy Graphene business.industry Scattering Deep learning Materials Science (cond-mat.mtrl-sci) Pattern recognition Materials Engineering 021001 nanoscience & nanotechnology Condensed Matter Physics cond-mat.mtrl-sci Nonlinear system machine learning Artificial intelligence Biochemistry and Cell Biology 0210 nano-technology business |
| Zdroj: | Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada, vol 27, iss 4 |
| Popis: | Phase contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of 2D materials such as monolayer graphene due to its high dose efficiency. However, phase contrast imaging can produce complex nonlinear contrast, even for weakly-scattering samples. It is therefore difficult to develop fully-automated analysis routines for phase contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method to a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily-adaptable source code for all results in this paper, and discuss potential applications for deep learning in fully-automated TEM image analysis. 12 pages, 6 figures |
| Databáze: | OpenAIRE |
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