|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Microstructure image analysis using deep convolutional neural networks
||Bo Lei, Elizabeth Holm
|On-Site Speaker (Planned)
In quantitative microscopy, microstructure image analysis lays the foundation for modeling the data and understanding the results. However, conventional image analysis methods are inefficient in handling complicated microstructure images and often require sophisticated and particular processing pipelines. We demonstrate that deep convolutional neural networks (DCNN) can be trained and achieve great performance and generality in challenging microstructure segmentation and analysis tasks. We evaluated and compared two DCNN models PixelNet and UNet with different training configurations to optimize the results on different datasets. The ability to segment complex microstructures enables a variety of new and high-throughput analysis methods. We also find that the quality of ground truth labels has a strong impact on performance, and reliable approaches to create ground truth labels are discussed.
||Planned: Supplemental Proceedings volume