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Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy

Author

Listed:
  • Chixiang Lu

    (The University of Hong Kong)

  • Kai Chen

    (The University of Hong Kong
    The University of Western Australia)

  • Heng Qiu

    (The University of Hong Kong)

  • Xiaojun Chen

    (The University of Western Australia)

  • Gu Chen

    (The University of Hong Kong)

  • Xiaojuan Qi

    (The University of Hong Kong)

  • Haibo Jiang

    (The University of Hong Kong)

Abstract

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 generates realistic predictions with high resolution ultrastructural details and exhibits robust transferability by taking only one pair of images of 3 megapixels to fine-tune in denoising and super-resolution tasks. EMDiffuse also demonstrated proficiency in the isotropic vEM reconstruction task, generating isotropic volume even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from seven public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate three-dimensional nanoscale ultrastructure analysis. EMDiffuse also features self-assessment functionalities on predictions’ reliability. We envision EMDiffuse to pave the way for investigations of the intricate subcellular nanoscale ultrastructure within large volumes of biological systems.

Suggested Citation

  • Chixiang Lu & Kai Chen & Heng Qiu & Xiaojun Chen & Gu Chen & Xiaojuan Qi & Haibo Jiang, 2024. "Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49125-z
    DOI: 10.1038/s41467-024-49125-z
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    References listed on IDEAS

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    1. Larissa Heinrich & Davis Bennett & David Ackerman & Woohyun Park & John Bogovic & Nils Eckstein & Alyson Petruncio & Jody Clements & Song Pang & C. Shan Xu & Jan Funke & Wyatt Korff & Harald F. Hess &, 2021. "Whole-cell organelle segmentation in volume electron microscopy," Nature, Nature, vol. 599(7883), pages 141-146, November.
    2. C. Shan Xu & Song Pang & Gleb Shtengel & Andreas Müller & Alex T. Ritter & Huxley K. Hoffman & Shin-ya Takemura & Zhiyuan Lu & H. Amalia Pasolli & Nirmala Iyer & Jeeyun Chung & Davis Bennett & Aubrey , 2021. "An open-access volume electron microscopy atlas of whole cells and tissues," Nature, Nature, vol. 599(7883), pages 147-151, November.
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