DeepEM Playground: Bringing deep learning to electron microscopy labs.

Journal: Journal of microscopy
Published Date: Jun 28, 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 customisation. 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.

Authors

  • Hannah Kniesel
    Visual Computing, University of Ulm, Germany.
  • Poonam Poonam
    Visual Computing Group, Ulm University, Ulm, Germany.
  • Tristan Payer
    Visual Computing Research Group, Institute of Media Informatics, Ulm University, Ulm, Germany.
  • Tim Bergner
    Central Facility for Electron Microscopy, Ulm University, Ulm, Germany.
  • Pedro Hermosilla
    Computer Vision Group, TU Vienna, Vienna, Austria.
  • Timo Ropinski
    Visual Computing Group, Institute of Media Informatics, Ulm University, Ulm, Germany.

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