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Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction
[article]
2021
arXiv
pre-print
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Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution. ...
Most approaches use discrete clustering which fails to capture the continuous nature of image rotation, others suffer from low-quality image reconstruction. ...
We compare our approach and the spatial-VAE method, which is considered a leading method in learning rotation-invariant features for Cryo-EM datasets. ...
arXiv:2101.03549v1
fatcat:gqbfz62nczbxldcabsddqqwwye
Imaging and characterizing cells using tomography
2015
Archives of Biochemistry and Biophysics
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SXT has a very high specimen throughput compared to other high-resolution structure imaging modalities; for example, tomographic data for reconstructing an entire eukaryotic cell is acquired in a matter ...
We can learn much about cell function by imaging and quantifying sub-cellular structures, especially if this is done non-destructively without altering said structures. ...
For the best possible tomographic reconstruction of the specimen, in addition to the specimen remaining invariant throughout imaging, the data should be as complete as possible. ...
doi:10.1016/j.abb.2015.01.011
pmid:25602704
pmcid:PMC4506273
fatcat:t6ycfl4nl5gm7juuhxv4z7jkyi
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
[article]
2021
arXiv
pre-print
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Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference ...
In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. ...
(b) spatial-VAE [43], disentangling translation/rotation features from different semantics. ...
arXiv:2105.08040v2
fatcat:56gnjk7y45a7jifx4s6npb6zxy
End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data
[article]
2021
arXiv
pre-print
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Cryogenic electron microscopy (cryo-EM) provides images from different copies of the same biomolecule in arbitrary orientations. ...
Here, we present an end-to-end unsupervised approach that learns individual particle orientations from cryo-EM data while reconstructing the average 3D map of the biomolecule, starting from a random initialization ...
ACKNOWLEDGMENTS We thank Wah Chiu, Khanh Dao Duc, Mark Hunter, TJ Lane, Julien Martel, Nina Miolane, and Gordon Wetzstein for numerous discussions that helped shape this project. ...
arXiv:2107.02958v1
fatcat:tdkzngji2zcsrogq3ykgrx6lb4
Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1911.08121v1 fatcat:sj7o2tgmcraytoningj64ukssy
Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at near-atomic resolution. ...
In this paper, we combine variational autoencoders (VAEs) and generative adversarial networks (GANs) to learn a low-dimensional latent representation of cryo-EM images. ...
Acknowledgments We thank Khanh Dao Duc and TJ Lane for very helpful comments on an earlier draft of the manuscript, and Daniel Ratner and Cornelius Gati for stimulating discussions and support throughout ...
arXiv:1911.08121v2
fatcat:l2snznfj6bgoznvkbotzyx4c6y
Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks
2020
IUCrJ
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In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline ...
Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions ...
Acknowledgements We thank Drs Joshua Batson, David Dynerman and David Agard for thoughtful discussions. The authors declare no competing interests related to this manuscript. ...
doi:10.1107/s2052252520013184
pmid:33209325
pmcid:PMC7642784
fatcat:kyyfxlfna5dddl6jx277hkcx6u
The Promise and the Challenges of Cryo‐Electron Tomography
2020
FEBS Letters
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Cryo-electron tomography combines the power of three-dimensional molecular level imaging with the best structural preservation that is physically possible to achieve. ...
Here we review state-of-the-art cryo-electron tomography workflows, provide examples of biological applications and discuss what is needed to realize the full potential of cryo-electron tomography. ...
We are grateful to Miroslava Schaffer for the cryo-FIB lift-out image, and to Ben Engel and Qiang Guo for providing the in situ tomography images. ...
doi:10.1002/1873-3468.13948
pmid:33020915
fatcat:ufibivczfvh4bfys2mhe5hsifi
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
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Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, ...
This doctoral thesis covers some of my advances in electron microscopy with deep learning. ...
Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article. ...
doi:10.5281/zenodo.4598227
fatcat:hm2ksetmsvf37adjjefmmbakvq
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
Preserved Fulltext
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Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, ...
This doctoral thesis covers some of my advances in electron microscopy with deep learning. ...
Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article. ...
doi:10.5281/zenodo.4591029
fatcat:zn2hvfyupvdwlnvsscdgswayci
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
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Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, ...
This doctoral thesis covers some of my advances in electron microscopy with deep learning. ...
Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article. ...
doi:10.5281/zenodo.4399748
fatcat:63ggmnviczg6vlnqugbnrexsgy
Computational Methods for Single-Particle Cryo-EM
[article]
2020
arXiv
pre-print
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Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic ...
This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that ...
Cryo-EM is transmission microscopy using electrons, which like X-rays allow angstromscale features to be detected. ...
arXiv:2003.13828v1
fatcat:sa4ifg3vhvhmreh3juxcciylla
Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2
2019
npj Computational Materials
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To circumvent this problem, we developed a deep learning framework for dynamic STEM imaging that is trained to find the structures (defects) that break a crystal lattice periodicity and apply it for mapping ...
Recent advances in scanning transmission electron microscopy (STEM) allow the real-time visualization of solid-state transformations in materials, including those induced by an electron beam and temperature ...
ACKNOWLEDGMENTS The work on microscopy and synthesis was supported by the U.S. ...
doi:10.1038/s41524-019-0152-9
fatcat:5krtkavwajekdpyodbj4n3eugq
A critical comparison between the two current methods of viewing frozen, live cells in the electron microscope: cryo-electron microscopic tomography versus "deep-etch" electron microscopy
2001
Biomedical Reviews
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Arguably, the greatest challenge facing electron microscopy (EM) today is to understand the basic structure or architecture of the cytoplasm, itself. ...
Electron microscopy can help to answer these questions, if it can reach the level of resolving individual macromolecules in whole cells, and if it can reach this level of resolution without at the same ...
anaglyphs" for publication, and Jennifer Scott for computer backup on every step of the production procedure. ...
doi:10.14748/bmr.v12.121
fatcat:jyjst2yfz5cb7mk35bbwcjzisi
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
Preserved Fulltext
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2021 accepted arXiv:2101.01178v4 fatcat:sgzeils4lzgp5ibelyrlvmh3o4Other Versions
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, ...
This doctoral thesis covers some of my advances in electron microscopy with deep learning. ...
Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article. ...
doi:10.5281/zenodo.4415407
fatcat:6dejwzzpmfegnfuktrld6zgpiq
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
Preserved Fulltext
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The file type is application/pdf.
Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, ...
This doctoral thesis covers some of my advances in electron microscopy with deep learning. ...
Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article. ...
doi:10.5281/zenodo.4413249
fatcat:35qbhenysfhvza2roihx52afuy
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