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

Chixiang Lu, Kai Chen, Heng Qiu, Xiaojun Chen, Gu Chen, Xiaojuan Qi () and Haibo Jiang ()
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Chixiang Lu: The University of Hong Kong
Kai Chen: The University of Hong Kong
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

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49125-z

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