  Quantification of microstructures is crucial for understanding
processing-structure and structure-property relationships in polycrystalline
materials. Delineating grain boundaries in bright-field transmission electron
micrographs, however, is challenging due to complex diffraction contrast in
images. Conventional edge detection algorithms are inadequate; instead, manual
tracing is usually required. This study demonstrates the first successful
machine-learning approach for grain-boundary detection in bright-field
transmission electron micrographs. The proposed methodology uses a U-Net
convolutional neural network trained on carefully constructed data from
bright-field images and hand-tracings available from prior studies, combined
with targeted post-processing algorithms to preserve fine features of interest.
The image processing pipeline accurately estimates grain-boundary positions,
avoiding segmentation in regions with intragrain contrast and identifying
low-contrast boundaries. Our approach is validated by directly comparing
microstructural markers (i.e., grain centroids) identified in U-Net predictions
with those identified in hand tracings; furthermore, the grain size
distributions obtained from the two techniques show notable overlap when
compared using t-, Kolmogorov-Smirnov, and Cramer-von Mises tests. The
technique is then successfully applied to interpret new aluminum film
microstructures having different image characteristics from the training data,
and preliminary results from Pt and Pd microstructures are presented,
highlighting the versatility of our approach for grain-boundary identification
in bright-field micrographs.
