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Peer-reviewedCC BY-NC-ND 4.0Cell Reports Methods

Volumetric denoising enables high-throughput volume electron microscopy and efficient downstream analysis

  • Bohao Chen1,2
  • Fangfang Wang3
  • Haoyu Wang3
  • Yanchao Zhang2,4
  • Zhuangzhuang Zhao2
  • Haoran Chen2,5
  • Hua Han2,4
  • Xi Chen2
  • Yunfeng Hua3
5 affiliations
  1. 1School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Science, Beijing, China
  2. 2State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  3. 3Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  4. 4School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
  5. 5School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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Abstract

Volume electron microscopy (VEM) enables nanometer-resolution three-dimensional (3D) visualization of biological specimens via serial sectioning and imaging. Owing to limitations of downstream analysis, VEM datasets are often acquired at slow speeds and high resolutions, thereby limiting achievable imaging throughput. By systematically searching for optimal VEM acquisition conditions, we find that sufficient spatial resolution effectively counteracts high image noise in preserving 3D structural information. To further verify that denoising is more effective in restoring volumetric datasets than axial interpolation, we compared machine learning-based methods, including a newly developed 3D context-based denoising model, through various tasks on VEM datasets acquired simultaneously. Our volumetric approach not only outperforms other baseline methods in faithful feature recovery but also facilitates robust serial block-face cutting down to 20 nm by allowing fast imaging. This work provides both an optimized acquisition strategy and volumetric denoising methods as actionable guidelines for maximizing VEM throughput.

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Cite

BibTeXRIS
BibTeX
@article{chen2026volumetric,
  title = {{Volumetric denoising enables high-throughput volume electron microscopy and efficient downstream analysis}},
  author = {Bohao Chen and Fangfang Wang and Haoyu Wang and Yanchao Zhang and Zhuangzhuang Zhao and Haoran Chen and Hua Han and Xi Chen and Yunfeng Hua},
  journal = {Cell Reports Methods},
  year = {2026},
  month = aug,
  doi = {10.1016/j.crmeth.2026.101543},
  url = {https://doi.org/10.1016/j.crmeth.2026.101543},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Volumetric denoising enables high-throughput volume electron microscopy and efficient downstream analysis
AU  - Bohao Chen
AU  - Fangfang Wang
AU  - Haoyu Wang
AU  - Yanchao Zhang
AU  - Zhuangzhuang Zhao
AU  - Haoran Chen
AU  - Hua Han
AU  - Xi Chen
AU  - Yunfeng Hua
PY  - 2026
DA  - 2026/08/04
JO  - Cell Reports Methods
DO  - 10.1016/j.crmeth.2026.101543
UR  - https://doi.org/10.1016/j.crmeth.2026.101543
AB  - Volume electron microscopy (VEM) enables nanometer-resolution three-dimensional (3D) visualization of biological specimens via serial sectioning and imaging. Owing to limitations of downstream analysis, VEM datasets are often acquired at slow speeds and high resolutions, thereby limiting achievable imaging throughput. By systematically searching for optimal VEM acquisition conditions, we find that sufficient spatial resolution effectively counteracts high image noise in preserving 3D structural information. To further verify that denoising is more effective in restoring volumetric datasets than axial interpolation, we compared machine learning-based methods, including a newly developed 3D context-based denoising model, through various tasks on VEM datasets acquired simultaneously. Our volumetric approach not only outperforms other baseline methods in faithful feature recovery but also facilitates robust serial block-face cutting down to 20 nm by allowing fast imaging. This work provides both an optimized acquisition strategy and volumetric denoising methods as actionable guidelines for maximizing VEM throughput.
SN  - 2667-2375
ER  -