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arXiv · 2501.03153

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

Alexander Wang·Max Xu·Risha Goel·Zain Shabeeb·Isabel Panicker·Vida Jamali
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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}.

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Alexander Wang, Max Xu, Risha Goel, Zain Shabeeb, Isabel Panicker, Vida Jamali. 2025-11-04. SAM-EM: Real-Time Segmentation for Automated Liquid Phase Transmission Electron Microscopy. https://arxiv.org/abs/2501.03153

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