Deep-layers-assisted machine learning for accurate image segmentation of complex materials

Abstract

In materials science, X-ray micro-computed tomography (μCT) imagery provides a unique tool for quantitative and qualitative characterization of structure and performance. The tomographic analysis relies on the segmentation of large data sets to produce quantifiable volumes, a highly time-intensive process. Great progress has been observed in developing robust and efficient segmentation techniques, and machine learning currently stands out as a powerful segmentation tool. These methods have demonstrated excellent performance when applied to various materials. However, the performance of such methods heavily relies on the amount of ground truth data. This presents a great challenge in image segmentation since the amount of a priori raw images can be limited by sample cost and size. Additionally, manual labeling of data sets is often very time-intensive and can be subjected to human error. Therefore, a machine learning method is needed which achieves high segmentation accuracies for reduced training data sets. To this end, we have offered a stochastic method for producing variations of the training images that will retain the important class-wide features and thereby enrich the machine learning's “understanding” of the variabilities. The proposed method can significantly increase the final overall accuracy. We found that by enlarging the initial training set by additional realizations, we were able to improve the average accuracy of segmentation from 81.1% to 90.0% for a very complex Mg-based alloy. The results of this study show that it is possible to increase the accuracy of predictions in imaging from X-ray microscopy using machine learning methods when enough data are not available. • Rapid and smart segmentation of multiphase complex materials is studied. • We presented a machine learning method that is combined with a stochastic method. • Our method shows a significant improvement over the original machine learning methods. • The proposed method can be used when enough data is not available for machine learning.