ArticleNature communications2024
A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography.
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 24 papers.
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Who cites it
24 citing papers in PubMed.
- High-resolution nuclear cell biology by cryo-electron tomography.Nucleus (Austin, Tex.) · 2026Review
- Sphingolipids associate with the chlamydial nucleoid and mark developmental transitions in Chlamydia trachomatis.Nature communications · 2026Article
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- CoCryoViS: Collaborative online cryo-electron tomography visualization system.Protein science : a publication of the Protein Society · 2026Article
- Electron tomography reveals mitochondrial network and cristae remodelling during cell differentiation in the human placenta.Nature communications · 2026Article
- RBMP2 shapes specialized membranes for CObioRxiv : the preprint server for biology · 2026Article
- In situ cryo-ET defines the ultrastructure of ER exit sites in human cells.Nature cell biology · 2026Article
- AreTomoLive: automated reconstruction of comprehensively corrected and denoised cryo-electron tomograms in real time and at high throughput.Nature methods · 2026Article
- A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.Bioinformatics (Oxford, England) · 2026Article
- Cryo-FIB-enabled 3D imaging of biological specimens.Applied microscopy · 2026Review
- Article
- ICECREAM: high-fidelity equivariant cryo-electron tomography.Acta crystallographica. Section D, Structural biology · 2026Article
- Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.Acta crystallographica. Section D, Structural biology · 2026Article
- Article
- Gradient based refinement of CryoET tilt series alignment improves tomogram contrast and structure resolution.bioRxiv : the preprint server for biology · 2026Article
- Article
- Dimeric gold nanoparticles enable multiplexed labeling in cryoelectron tomography.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Spatial constraints drive amylosome-mediated resistant starch degradation by Ruminococcus bromii in the human colon.Nature communications · 2025Article
- Structure and assembly of the A-C linker connecting microtubule triplets in centrioles.Science advances · 2025Article
- cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography.Communications biology · 2025Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cryogenic electron tomography is a technique for imaging biological samples in 3D. A microscope collects a series of 2D projections of the sample, and the goal is to reconstruct the 3D density of the sample called the tomogram. Reconstruction is difficult as the 2D projections are noisy and can not be recorded from all directions, resulting in a missing wedge of information. Tomograms conventionally reconstructed with filtered back-projection suffer from noise and strong artefacts due to the missing wedge. Here, we propose a deep-learning approach for simultaneous denoising and missing wedge reconstruction called DeepDeWedge. The algorithm requires no ground truth data and is based on fitting a neural network to the 2D projections using a self-supervised loss. DeepDeWedge is simpler than current state-of-the-art approaches for denoising and missing wedge reconstruction, performs competitively and produces more denoised tomograms with higher overall contrast.
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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.