Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning
| Autor: | Doga Gursoy, C. Shashank Kaira, Francesco De Carlo, Xiaogang Yang, Nikhilesh Chawla, Vincent De Andrade, William Scullin |
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| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Correlative Materials science business.industry Mechanical Engineering Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Convolutional neural network Transmission (telecommunications) Mechanics of Materials 0103 physical sciences Microscopy General Materials Science Segmentation Tomography Artificial intelligence 0210 nano-technology business |
| Zdroj: | Materials Characterization. 142:203-210 |
| ISSN: | 1044-5803 |
| DOI: | 10.1016/j.matchar.2018.05.053 |
| Popis: | A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps. |
| Databáze: | OpenAIRE |
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