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

arXiv: https://arxiv.org/abs/2501.03153 · HTML: https://mlsanity.com/paper/2501.03153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Learning Video Object Segmentation from Static Images](https://mlsanity.com/paper/1612.02646/) (2016)
- [Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis](https://mlsanity.com/paper/2204.04250/) (2022)
- [A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM)](https://mlsanity.com/paper/2309.06122/) (2023)

## Abstract (arXiv)

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: hrefhttps://github.com/JamaliLab/SAM-EMgithub.com/JamaliLab/SAM-EM.