DeepEM Playgound: Bringing Deep Learning to Electron Microscopy Labs

Journal of Microscopy 2025

Abstract

Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. This is largely due to the inaccessibility of DL methods for EM specialists and the expertise required to interpret model outputs. To bridge this gap, we introduce DeepEM Playground, an interactive, user-friendly platform designed to empower EM researchers—regardless of coding experience—to train, tune, and apply DL models. By providing a guided, hands-on approach, DeepEM Playground enables users to explore the workings of DL in EM, facilitating both first-time engagement and more advanced model customization. The DeepEM Playground lowers the barrier to entry and fosters a deeper understanding of deep learning, thereby enabling the EM community to integrate AI-driven analysis into their workflows more confidently and effectively.

Bibtex

@article{kniesel2025deepem,
	title={DeepEM Playgound: Bringing Deep Learning to Electron Microscopy Labs},
	author={Kniesel, Hannah and Poonam, Poonam and Payer, Tristan and Bergner, Tim and Hermosilla, Pedro and Ropinski, Timo},
	year={2025},
	journal={Journal of Microscopy},
	issue={Festschrift for Paul Walther},
	doi={10.1111/jmi.70005}
}