DeepEM Playground: Bringing deep learning to electron microscopy labs

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.

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Publication Details

Journal
Journal of Microscopy
Published
2025-06-28
DOI
https://doi.org/10.1111/jmi.70005
Citations
1
Primary Topic
Electron and X-Ray Spectroscopy Techniques
Type
article
Field-Weighted Citation Impact
0.66
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article

DeepEM Playground: Bringing deep learning to electron microscopy labs

Hannah Kniesel, Tim Bergner, Timo Ropinski, Poonam Poonam et al.
1 citations
Journal of Microscopy
Electron and X-Ray Spectroscopy Techniques
0.66
article

DeepEM Playground: Bringing deep learning to electron microscopy labs

Hannah Kniesel, Tim Bergner, Timo Ropinski, Poonam Poonam, Tristan Payer, Pedro Hermosilla
article en
1 citations

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.

Journal of MicroscopyVol. 299(3)
TU Wien (AT), Universität Ulm (DE)
Openalex Percentile: Top 31%
Electron and X-Ray Spectroscopy Techniques
0.66
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DeepEM Playground: Bringing deep learning to electron microscopy labs — Hannah Kniesel, Tim Bergner, et al. · Journal of Microscopy (2025) | TGRS Research Map | TGRS