CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy

• Published in: Communications biology 2021-02, Vol.4 (1), p.200-200, Article 200
• Main Authors: George, Blesson, Assaiya, Anshul, Roy, Robin J., Kembhavi, Ajit, Chauhan, Radha, Paul, Geetha, Kumar, Janesh, Philip, Ninan S.
• Format: Article
• Language: English
• Subjects: 101/28; 631/114/1314; 631/1647/2258/1258/1259; Algorithms; Animals; Automation; Biology; Biomedical and Life Sciences; Cryoelectron Microscopy; Data processing; Deep Learning; Electron microscopy; Humans; Image processing; Image Processing, Computer-Assisted; Impurities; Life Sciences; Macromolecules; Micrography; Microscopy; Models, Molecular; Protein Conformation; Proteins; Proteins - ultrastructure; Segmentation; Semantics; Single Molecule Imaging
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-021-01721-1

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. George, Assaiya et al. develop a deep learning tool, CASSPER, that automates the detection of protein particles in transmission microscope images. This algorithm uses semantic segmentation and visually prepared training samples to capture the differences in the transmission intensities of microscope images, enabling automation of data processing.