Human perception-inspired grain segmentation refinement using conditional random fields

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 by correctly reconstructing fragmented grain boundaries in electron microscopy images of a polycrystalline oxide. The refinement improves the statistical representation of the microstructure, reflected by a 51 % improvement in a grain alignment metric that provides a more physically meaningful assessment of complex microstructures than conventional metrics. This method enables rapid and accurate characterization, facilitating an unprecedented level of data analysis and improving the understanding of grain boundary networks, making it suitable for a range of disciplines where precise segmentation of interconnected line networks is essential. • Enhanced Segmentation Accuracy: The post-processing method significantly improves segmentation accuracy, as evidenced by metrics commonly used for segmentation tasks, like pixel accuracy, intersection-over-union (IoU), and Dice similarity coefficient (DSC). • Time Efficiency: The method drastically reduces the time required for grain segmentation compared to manual labeling, which is crucial for applications requiring real-time analysis, such as in-situ materials science experiments. • New Grain Alignment Metric: The development of a novel grain alignment metric offers a more robust measure for segmented region accuracy, especially useful in thin segmentation masks. • Applicability Across Materials and Domains: Unlike prior custom post-processing approaches, this methodology is robust and generalizable and can be applied to a wide range of crystalline materials, enhancing its utility in materials science research.