WikiSysop: Created page with "== Citation == Huang, Qinwen / Zhou, Ye / Liu, Hsuan-Fu / Bartesaghi, Alberto. Joint micrograph denoising and protein localization in cryo-electron microscopy. 2024. Biological Imaging, Vol. 4 == Abstract == Cryo-electron microscopy (cryo-EM) is an imaging technique that allows the visualization of proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, result in images where t..."

2024-08-06T07:01:53Z

Created page with "== Citation == Huang, Qinwen / Zhou, Ye / Liu, Hsuan-Fu / Bartesaghi, Alberto. Joint micrograph denoising and protein localization in cryo-electron microscopy. 2024. Biological Imaging, Vol. 4 == Abstract == Cryo-electron microscopy (cryo-EM) is an imaging technique that allows the visualization of proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, result in images where t..."

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== Citation ==

Huang, Qinwen / Zhou, Ye / Liu, Hsuan-Fu / Bartesaghi, Alberto. Joint micrograph denoising and protein localization in cryo-electron microscopy. 2024. Biological Imaging, Vol. 4

== Abstract ==

Cryo-electron microscopy (cryo-EM) is an imaging technique that allows the visualization of proteins and macromolecular
complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the
biological samples, result in images where the power of noise is 100 times stronger than that of the signal. Accurate
identification of proteins from these low signal-to-noise ratio (SNR) images is a critical task, as the detected positions
serve as inputs for the downstream 3D structure determination process. Current methods either fail to identify
all true positives or result in many false-positives, especially when analyzing images from smaller-sized proteins
that exhibit extremely low contrast, or require manual labeling that can take days to complete. Acknowledging the
fact that accurate protein identification is dependent upon the visual interpretability of micrographs, we propose
a framework that can perform denoising and detection in a joint manner and enable particle localization under
extremely low SNR conditions using self-supervised denoising and particle identification from sparsely annotated
data. We validate our approach on three challenging single-particle cryo-EM datasets and projection images from
one cryo-electron tomography (cryo-ET) dataset with extremely low SNR, showing that it outperforms existing
state-of-the-art methods used for cryo-EM image analysis by a significant margin. We also evaluate the performance
of our algorithm under decreasing SNR conditions and show that our method is more robust to noise than
competing methods.

== Keywords ==

== Links ==

https://www.cambridge.org/core/journals/biological-imaging/article/joint-micrograph-denoising-and-protein-localization-in-cryoelectron-microscopy/7C6A241C84356D2430B35E592C4FEFA6

== Related software ==

== Related methods ==

== Comments ==

2024Huang Joint - Revision history