SAM-EM: Real-Time Segmentation for Automated Liquid Phase Transmission Electron Microscopy

Abstract

The absence of robust segmentation frameworks for noisy liquid phase transmission electron microscopy (LPTEM) videos prevents reliable extraction of particle trajectories, creating a major barrier to quantitative analysis and to connecting observed dynamics with materials characterization and design. To address this challenge, we present Segment Anything Model for Electron Microscopy (SAM-EM), a domain-adapted foundation model that unifies segmentation, tracking, and statistical analysis for LPTEM data. Built on Segment Anything Model 2 (SAM~2), SAM-EM is derived through full-model fine-tuning on 46,600 curated LPTEM synthetic video frames, substantially improving mask quality and temporal identity stability compared to zero-shot SAM~2 and existing baselines. Beyond segmentation, SAM-EM integrates particle tracking with statistical tools, including mean-squared displacement and particle displacement distribution analysis, providing an end-to-end framework for extracting and interpreting nanoscale dynamics. Crucially, full fine-tuning allows SAM-EM to remain robust under low signal-to-noise conditions, such as those caused by increased liquid sample thickness in LPTEM experiments. By establishing a reliable analysis pipeline, SAM-EM transforms LPTEM into a quantitative single-particle tracking platform and accelerates its integration into data-driven materials discovery and design. Project page: \href{https://github.com/JamaliLab/SAM-EM}{github.com/JamaliLab/SAM-EM}.