| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Machine Learning Enabled Reproducible Data Analysis for Electron Microscopy |
| Author(s) |
Xiaoting Zhong, Nestor Zaluzec, Yu Lin, Jiadong Gong |
| On-Site Speaker (Planned) |
Yu Lin |
| Abstract Scope |
We show that region-based convolutional neural network (R-CNN) models can detect/segment complicated microstructure features in two distinct material systems using a few (<20) training images. Our first material system contains biological vesicles. It is imaged using transmission electron microscopy (TEM) bright field mode. The vesicles images are challenging because they have low contrast. We trained a mask-RCNN and achieved an 86.64 % validation mean average precision (mAP) for the large vesicle segmentation task. Our second material system contains nano-rods. It is imaged using the high angle annular dark field (HAADF) technique. The nano-rod images are challenging because individual nano-rods highly overlap each other. We trained a faster-RCNN model and achieved a 50.51 % validation mAP for the nano-rod detection task. Accurate and reproducible microstructure statistics can be computed from the ML processed TEM images with fast speed. This approach opens exciting opportunities for automated EM image analysis. |