Deep learning for scanning electron microscopy: Synthetic data for the nanoparticles detection

• Published in: Ultramicroscopy Vol. 219; p. 113125
• Main Author: Kharin, A. Yu
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2020
• Subjects: convolutional neural networks; deep learning; detection; Image processing; nanoparticles; scanning electron microscopy; synthetic data; scanning electron microscopy; deep learning; detection; nanoparticles; Image processing; synthetic data; convolutional neural networks
• ISSN: 0304-3991; 1879-2723; 1879-2723
• DOI: 10.1016/j.ultramic.2020.113125

•Training data for deep learning algorithms can be produced synthetically•Synthetic data based on real SEM images as textures has been obtained•RetinaNet Convolutional network trained on synthetic data can be successfully used on real SEM images Deep learning algorithms are one of most rapid developing fields into the modern computation technologies. One of the bottlenecks into the implementation of such advaced algorithms is their requirement for a large amount of manually-labelled data for training. For the general-purpose tasks, such as general purpose image classification/detection the huge images datasets are already labelled and collected. For more subject specific tasks (such as electron microscopy images treatment), no labelled data available. Here I demonstrate that a deep learning network can be successfully trained for nanoparticles detection using semi-synthetic data. The real SEM images were used as a textures for rendered nanoparticles at the surface. Training of RetinaNet architecture using transfer learning can be helpful for the large-scale particle distribution analysis. Beyond such applications, the presented approach might be applicable to other tasks, such as image segmentation.