🤖 AI Summary
To address inaccurate statistical characterization caused by fragmented and poorly connected grain boundary segmentation in electron microscopy (EM) images of polycrystalline materials, this paper proposes a general-purpose, human visual perception–inspired post-processing method. The approach uniquely integrates perceptual grouping principles with conditional random fields (CRFs) to explicitly model the topological connectivity of grain boundaries. Furthermore, it introduces a novel grain boundary alignment metric that is independent of specific segmentation models. Taking U-Net outputs as input, the method achieves 79% accuracy in grain boundary segment identification and improves grain boundary alignment by 51% on oxide polycrystalline EM images. This significantly enhances both the structural integrity of reconstructed grain boundary networks and their reliability for quantitative microstructural analysis.
📝 Abstract
Automated detection of grain boundaries in electron microscope images of polycrystalline materials could help accelerate the nanoscale characterization of myriad engineering materials and novel materials under scientific research. Accurate segmentation of interconnected line networks, such as grain boundaries in polycrystalline material microstructures, poses a significant challenge due to the fragmented masks produced by conventional computer vision algorithms, including convolutional neural networks. These algorithms struggle with thin masks, often necessitating post-processing for effective contour closure and continuity. Previous approaches in this domain have typically relied on custom post-processing techniques that are problem-specific and heavily dependent on the quality of the mask obtained from a computer vision algorithm. Addressing this issue, this paper introduces a fast, high-fidelity post-processing technique that is universally applicable to segmentation masks of interconnected line networks. Leveraging domain knowledge about grain boundary connectivity, this method employs conditional random fields and perceptual grouping rules to refine segmentation masks of any image with a discernible grain structure. This approach significantly enhances segmentation mask accuracy, achieving a 79% segment identification accuracy in validation with a U-Net model on electron microscopy images of a polycrystalline oxide. Additionally, a novel grain alignment metric is introduced, showing a 51% improvement in grain alignment. This method not only enables rapid and accurate segmentation but also facilitates an unprecedented level of data analysis, significantly improving the statistical representation of grain boundary networks, making it suitable for a range of disciplines where precise segmentation of interconnected line networks is essential.
