Physics Informed Generative AI Enabling Labour Free Segmentation For Microscopy Analysis

TL;DR

This paper presents a physics-informed generative AI framework that creates realistic microscopy images from simulations, enabling fully automated, labour-free segmentation with high accuracy, thus overcoming data scarcity and domain gap issues.

Contribution

It introduces a novel pipeline combining phase-field simulations, CycleGAN translation, and a U-Net model for accurate segmentation without manual annotations.

Findings

Achieved a mean Boundary F1-Score of 0.90 on experimental images.

Generated synthetic SEM images indistinguishable from real data.

Demonstrated effective generalisation of the segmentation model to unseen data.

Abstract

Semantic segmentation of microscopy images is a critical task for high-throughput materials characterisation, yet its automation is severely constrained by the prohibitive cost, subjectivity, and scarcity of expert-annotated data. While physics-based simulations offer a scalable alternative to manual labelling, models trained on such data historically fail to generalise due to a significant domain gap, lacking the complex textures, noise patterns, and imaging artefacts inherent to experimental data. This paper introduces a novel framework for labour-free segmentation that successfully bridges this simulation-to-reality gap. Our pipeline leverages phase-field simulations to generate an abundant source of microstructural morphologies with perfect, intrinsically-derived ground-truth masks. We then employ a Cycle-Consistent Generative Adversarial Network (CycleGAN) for unpaired image-to-image translation, transforming the clean simulations into a large-scale dataset of high-fidelity, realistic SEM images. A U-Net model, trained exclusively on this synthetic data, demonstrated remarkable generalisation when deployed on unseen experimental images, achieving a mean Boundary F1-Score of 0.90 and an Intersection over Union (IOU) of 0.88. Comprehensive validation using t-SNE feature-space projection and Shannon entropy analysis confirms that our synthetic images are statistically and featurally indistinguishable from the real data manifold. By completely decoupling model training from manual annotation, our generative framework transforms a data-scarce problem into one of data abundance, providing a robust and fully automated solution to accelerate materials discovery and analysis.