ArticleNature communications2024
Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Beyond structure: spectroscopic imaging in cryogenic electron microscopy.Current opinion in structural biology · 2026Review
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images.bioRxiv : the preprint server for biology · 2026Article
- vEMRec: High-Resolution Volume Electron Microscopy Reconstruction Based on Structure-Preserving and High-Fidelity 3D Alignment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Targeting organelle function in T cells for cancer immunotherapy.Nature reviews. Immunology · 2026Review
- vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Volume electron microscopy in axon regeneration research: insights into mitochondria, endoplasmic reticulum, and membrane contacts.Frontiers in neural circuits · 2026Review
- Seeing the Middle: Reconstructing 3D Internal Electrode Microstructures from Low-Resolution Surfaces with Generative Diffusion Artificial Intelligence.Small science · 2025Article
- Deep learning for sub-ångström-resolution imaging in uncorrected scanning transmission electron microscopy.National science review · 2025Article
- PSSR2: a user-friendly Python package for democratizing deep learning-based point-scanning super-resolution microscopy.BMC methods · 2025Article
- A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis.Frontiers in medicine · 2025Review
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy.Nature communications · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.