A unified microstructure segmentation approach via human-in-the-loop machine learning

Abstract

Microstructure segmentation, a technique for extracting structural statistics from microscopy images, is an essential step for establishing quantitative structure–property relationships in a broad range of materials research areas. However, the task is challenging due to the morphological complexity and diversity of structural constituents. While recent breakthroughs in deep learning have led to significant progress in microstructure segmentation, there remain two fundamental challenges to its further diffusion: prohibitive annotation costs and an unreliable decision-making process. To tackle these challenges, we propose a human-in-the-loop machine learning framework for unified microstructure segmentation, which leverages recent advances in both weakly supervised learning and active learning techniques. The key idea behind our approach lies in the integration of human and machine capabilities to make not only precise but also reliable microstructure segmentation at minimal annotation costs. Extensive experiments demonstrate the generality of our framework across different material classes, structural constituents, and microscopic imaging modalities.