Electron Microscopy

Latest papers

36 papers5 papers in the last four weeks, down 37% on the four weeks before. 0.0% of all new papers.

Bridging the Simulation-to-Reality Gap in Electron Microscope Calibration via VAE-EM Estimation
Mar 17, 2026Jilles S. van Hulst, W. P. M. H. Heemels, Duarte J. AntunesElectron Microscopy Scanning Transmission Electron Microscopy
The authors are with the Control Systems Technology Section, Department of Mechanical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction
Feb 25, 2026Jonathan Krook, Axel Janson, Joakim Andén +2Cryo-Electron Microscopy Electron Microscopy
KTH Royal Institute of Technology · Harvard University Department of Mathematics School of Engineering and Applied Sciences
CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints
Feb 25, 2026Fuyao Huang, Xiaozhu Yu, Kui Xu +1Cryo-Electron Microscopy Electron Microscopy
State Key Laboratory of Membrane Biology–Membrane Structure and Artificial Intelligence Biology Branch, Hangzhou, China · State Key Laboratory of Membrane Biology, Beijing Tsinghua Institute for Frontier Interdisciplinary Innovation, Beijing, China · State Key Laboratory of Membrane Biology, Beijing Frontier Research Center of Biological Structures, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
Region of Interest Segmentation and Morphological Analysis for Membranes in Cryo-Electron Tomography
Feb 24, 2026Xingyi Cheng, Julien Maufront, Aurélie Di Cicco +3Image Segmentation Electron Microscopy
Institut Curie, CNRS UMR168, Université PSL, Sorbonne Université, Paris, France · Inria Center at University of Rennes, SAIRPICO team, Rennes, France · Leiden Institute of Advanced Computer Science (LIACS), Universiteit Leiden, the Netherlands
EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation
Aug 22, 2025Nan Wang, Zhiyi Xia, Yiming Li +7Synthetic Data Augmentation Instance Segmentation
DP Technology, Beijing, China
Segment Anything for Dendrites from Electron Microscopy
Nov 4, 2024Zewen Zhuo, Ilya Belevich, Ville Leinonen +4Image Segmentation Vision Foundation Models
A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio, Finland · Electron Microscopy Unit Institute of Biotechnology University of Helsinki Helsinki, Finland · Department of Medicine Faculty of Health Sciences University of Eastern Finland Kuopio, Finland

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Bridging the Simulation-to-Reality Gap in Electron Microscope Calibration via VAE-EM Estimation

Jilles S. van Hulst, W. P. M. H. Heemels, Duarte J. Antunes

The authors are with the Control Systems Technology Section, Department of Mechanical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

Electron Microscopy Scanning Transmission Electron Microscopy Microscopy Image Analysis Sim-to-Real Transfer

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Electron microscopy has enabled many scientific breakthroughs across multiple fields. A key challenge is the tuning of microscope parameters based on images to overcome optical aberrations that deteriorate image quality. This calibration problem is challenging due to the high-dimensional and noisy nature of the diagnostic images, and the fact that optimal parameters cannot be identified from a single image. We tackle the calibration problem for Scanning Transmission Electron Microscopes (STEM) by employing variational autoencoders (VAEs), trained on simulated data, to learn low-dimensional representations of images, whereas most existing methods extract only scalar values. We then simultaneously estimate the model that maps calibration parameters to encoded representations and the optimal calibration parameters using an expectation maximization (EM) approach. This joint estimation explicitly addresses the simulation-to-reality gap inherent in data-driven methods that train on simulated data from a digital twin. We leverage the known symmetry property of the optical system to establish global identifiability of the joint estimation problem, ensuring that a unique optimum exists. We demonstrate that our approach is substantially faster and more consistent than existing methods on a real STEM, achieving a 2x reduction in estimation error while requiring fewer observations. This represents a notable advance in automated STEM calibration and demonstrates the potential of VAEs for information compression in images. Beyond microscopy, the VAE-EM framework applies to inverse problems where simulated training data introduces a reality gap and where non-injective mappings would otherwise prevent unique solutions.

Latest papers

Bridging the Simulation-to-Reality Gap in Electron Microscope Calibration via VAE-EM Estimation

Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction

CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints

Region of Interest Segmentation and Morphological Analysis for Membranes in Cryo-Electron Tomography

EM3M: An Electron Micrograph Dataset for Microstructural Segmentation and Generation

Segment Anything for Dendrites from Electron Microscopy