Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials
Journal:
arXiv
Published Date:
Feb 20, 2025
Abstract
Understanding the relationship between the evolution of microstructures of
irradiated LiAlO2 pellets and tritium diffusion, retention and release could
improve predictions of tritium-producing burnable absorber rod performance.
Given expert-labeled segmented images of irradiated and unirradiated pellets,
we trained Deep Convolutional Neural Networks to segment images into defect,
grain, and boundary classes. Qualitative microstructural information was
calculated from these segmented images to facilitate the comparison of
unirradiated and irradiated pellets. We tested modifications to improve the
sensitivity of the model, including incorporating meta-data into the model and
utilizing uncertainty quantification. The predicted segmentation was similar to
the expert-labeled segmentation for most methods of microstructural
qualification, including pixel proportion, defect area, and defect density.
Overall, the high performance metrics for the best models for both irradiated
and unirradiated images shows that utilizing neural network models is a viable
alternative to expert-labeled images.
