Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data
| Autor: | Mary Scott, Catherine Groschner, Christina Choi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Pipeline (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Image processing 02 engineering and technology 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Sørensen–Dice coefficient FOS: Electrical engineering electronic engineering information engineering Segmentation High-resolution transmission electron microscopy Instrumentation business.industry Deep learning Image and Video Processing (eess.IV) Image segmentation Electrical Engineering and Systems Science - Image and Video Processing 021001 nanoscience & nanotechnology 0104 chemical sciences 3. Good health Random forest Artificial intelligence 0210 nano-technology business computer |
| Popis: | In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two step pipeline for analysis of high resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape and defect presence, enabling detection of correlations between these features. 10 pages, 5 figures |
| Databáze: | OpenAIRE |
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