Human Perception-Inspired Grain Segmentation Refinement Using
  Conditional Random Fields
Doruk Aksoy Huolin L. Xin Timothy J. Rupert William J. Bowman
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 intricate post-processing for effective
contour closure and continuity. Addressing this issue, this paper introduces a
fast, high-fidelity post-processing technique, leveraging domain knowledge
about grain boundary connectivity and employing conditional random fields and
perceptual grouping rules. 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, providing a more detailed assessment of
segmentation performance for complex microstructures. 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.