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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

PIXER: an automated particle-selection method based on segmentation using a deep neural network.

Jingrong Zhang^1,2, Zihao Wang^1,2, Yu Chen^1,2

¹High Performance Computer Research Center, Institute of Computing Technology Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, China.

BMC Bioinformatics

|January 20, 2019

Summary

This summary is machine-generated.

We developed PIXER, an automated deep learning method for selecting particles in cryo-electron microscopy (cryo-EM) images. PIXER overcomes low signal-to-noise challenges, offering accurate particle selection comparable to semi-automated methods.

Keywords:

Cryo-electron microscope Deep learning Particle selection Segmentation Single-particle analysis

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Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Cryo-electron microscopy (cryo-EM) is crucial for determining protein structures.
Automated particle selection from low signal-to-noise ratio (SNR) micrographs remains a challenge.
Current methods require laborious manual intervention or are insufficient for research needs.

Purpose of the Study:

To develop a fully automated particle selection method for cryo-EM.
To improve the efficiency and accuracy of particle selection in low-SNR conditions.
To reduce the manual workload for researchers in cryo-EM data processing.

Main Methods:

Developed PIXER, a deep neural network-based segmentation method.
Generated an automated method for creating cryo-EM segmentation training datasets.
Utilized probability density maps and a grid-based, local-maximum approach for particle localization.

Main Results:

PIXER converts micrographs into probability density maps, enhancing particle signals.
The method successfully generates training data for segmentation tasks.
PIXER demonstrated comparable performance to semi-automated methods (RELION, DeepEM) on simulated and real datasets.

Conclusions:

PIXER is the first automated particle selection method using deep segmentation networks for cryo-EM.
It significantly reduces manual labor in particle selection.
PIXER provides accurate results rapidly, even in low-SNR environments.