EMDiffuse: a diffusion-based deep learning method augmenting ultrastructural imaging and volume electron microscopy Article Swipe
Chixiang Lu
,
Kai Chen
,
Heng Qiu
,
Xiaojun Chen
,
Chen Gu
,
Xiaojuan Qi
,
Haibo Jiang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.07.12.548636
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.07.12.548636
Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse demonstrates outstanding denoising and super-resolution performance, generates realistic predictions without unwarranted smoothness, improves predictions’ resolution by ∼30%, and exhibits excellent transferability by taking only one pair of images to fine-tune. EMDiffuse also pioneers the isotropic vEM reconstruction task, generating isotropic volume similar to that obtained using advanced FIB-SEM even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from six public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate organelle reconstruction, making 3D nanoscale ultrastructure analysis faster and more accessible and extending such capability to larger volumes. More importantly, EMDiffuse features self-assessment functionalities and guarantees reliable predictions for all tasks. We envision EMDiffuse to pave the way for more in-depth investigations into the intricate subcellular nanoscale structures within large areas and volumes of biological systems.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.07.12.548636
- https://www.biorxiv.org/content/biorxiv/early/2023/07/12/2023.07.12.548636.full.pdf
- OA Status
- green
- Cited By
- 5
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384133559
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384133559Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.07.12.548636Digital Object Identifier
- Title
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EMDiffuse: a diffusion-based deep learning method augmenting ultrastructural imaging and volume electron microscopyWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-12Full publication date if available
- Authors
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Chixiang Lu, Kai Chen, Heng Qiu, Xiaojun Chen, Chen Gu, Xiaojuan Qi, Haibo JiangList of authors in order
- Landing page
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https://doi.org/10.1101/2023.07.12.548636Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/07/12/2023.07.12.548636.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2023/07/12/2023.07.12.548636.full.pdfDirect OA link when available
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Isotropy, Robustness (evolution), Computer science, Volume (thermodynamics), Artificial intelligence, Ultrastructure, Computer vision, Physics, Optics, Chemistry, Biology, Quantum mechanics, Anatomy, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 2, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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