CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy
| Autor: | Blesson George, Radha Chauhan, Geetha Paul, Ninan Sajeeth Philip, Janesh Kumar, Ajit Kembhavi, Anshul Assaiya, Robin Jacob Roy |
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| Rok vydání: | 2020 |
| Předmět: |
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Molecular Microscope Computer science Cryo-electron microscopy QH301-705.5 Protein Conformation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Medicine (miscellaneous) Data_CODINGANDINFORMATIONTHEORY General Biochemistry Genetics and Molecular Biology Particle identification Article law.invention 03 medical and health sciences 0302 clinical medicine Deep Learning law Cryoelectron microscopy Microscopy Image Processing Computer-Assisted Animals Humans Segmentation Biology (General) 030304 developmental biology 0303 health sciences business.industry Proteins Pattern recognition Single Molecule Imaging Semantics Data processing Transmission (telecommunications) Particle Adaptive histogram equalization Artificial intelligence General Agricultural and Biological Sciences business 030217 neurology & neurosurgery |
| Zdroj: | Communications Biology Communications Biology, Vol 4, Iss 1, Pp 1-12 (2021) |
| ISSN: | 2399-3642 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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