George, B and Assaiya, A and Roy, RJ and Kembhavi, A and Chauhan, R and Paul, G and Kumar, J and Philip, NS (2021) A Semantic Segmentation based Particle Picking Algorithm for Single Particle Cryo-Electron Microscopy. Communication Biology, 4. p. 200.

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Abstract

Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determination. Here, we present a generalized deep learning tool, CASSPER, for the automated detection and isolation of protein particles in transmission microscope images. This deep learning tool uses Semantic Segmentation and a collection of visually prepared training samples to capture the differences in the transmission intensities of protein, ice, carbon, and other impurities found in the micrograph. CASSPER is a semantic segmentation based method that does pixel-level classification and completely eliminates the need for manual particle picking. Integration of Contrast Limited Adaptive Histogram Equalization (CLAHE) in CASSPER enables high-fidelity particle detection in micrographs with variable ice thickness and contrast. A generalized CASSPER model works with high efficiency on unseen datasets and can potentially pick particles on-the-fly, enabling data processing automation.

Item Type:	Article
Subjects:	Cell Biology
Depositing User:	Mr. Rameshwar Nema
Date Deposited:	19 Apr 2021 10:30
Last Modified:	29 Nov 2021 07:48
URI:	http://nccs.sciencecentral.in/id/eprint/915

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