Single-particle image processing method and device, electronic equipment and storage medium

By developing a single-particle image denoising model that is trained unsupervised on cryo-electron microscopy images, and utilizing CTF convolutional kernels and neural networks to process single-particle images, this model solves the problem of severe noise interference in existing technologies, enabling clearer image observation and more efficient protein structure analysis.

CN116453115BActive Publication Date: 2026-07-24BIOMAP (BEIJING) INTELLIGENCE TECH LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
BIOMAP (BEIJING) INTELLIGENCE TECH LTD
Filing Date
2023-04-21
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing single-particle image processing methods cannot effectively remove noise from cryo-electron microscopy images, making it difficult to clearly observe protein structure and morphology.

Method used

An unsupervised training method was adopted, using a set of single-particle images output by cryo-electron microscopy to train the single-particle image denoising model. Image denoising was performed using CTF convolutional kernels and neural networks, including encoding and decoding processes, to learn the three-dimensional structure of sample membrane proteins and generate clear two-dimensional images.

Benefits of technology

It significantly improves the clarity of cryo-electron microscopy images, enabling clearer observation of particle structure and morphology, and enhancing the effectiveness of single-particle analysis.

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Abstract

The present disclosure provides a single-particle image processing method, device, electronic equipment and storage medium. A specific embodiment of the method comprises: obtaining a single-particle image set output by a cryo-electron microscope for a sample membrane protein; training a single-particle image denoising model using the single-particle image set, so that the single-particle image denoising model can learn the three-dimensional structure of the sample membrane protein; inputting a first single-particle image into the trained single-particle image denoising model to obtain a denoised first single-particle image corresponding to the first single-particle image, the first single-particle image being a single-particle image in the single-particle image set. This embodiment improves the clarity of denoising the single-particle image.
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