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Organizers: Joakim Andén, Dorit Hanein, Roy R. Lederman and Steven J. Ludtke

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Niels Volkmann
Professor of Electrical and Computer Engineering
UC Santa Barbara

Date and time: October 2th, 2024 Wednesday, at 8am PDT / 11am EDT / 4pm BST / 5pm CEST / 11pm China CST

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High(er) throughput analysis of actin-filament structures in cellular tomograms

The actin cytoskeleton plays a key role in cell migration and morphology in eukaryotic cells. Its diverse architectures enable functions such as protrusion, adhesion, contraction, and retraction. Analyzing cryo-ET data presents challenges due to low signal-to-noise ratios, stemming from weak contrast between biomolecules and the surrounding medium, as well as low electron doses to prevent sample damage. Current methods, such as analyzing vectorized actin filament traces or subtomogram averaging, have provided detailed insights into cytoskeletal structures but do not scale well for large datasets. In this talk, I will present alternative higher-throughput methods we developed for extracting nanoscale actin-filament parameters from hundreds of tomograms.

Last Talk

Fast Normalized Cross-Correlation for Template Matching with Rotations

Object detection is a main task in computer vision. Template matching is the reference method for detecting objects with arbitrary templates. However, template matching computational complexity depends on the rotation accuracy, being a limiting factor for large 3D images (tomograms). 

Here, we implement a new algorithm called tensorial template matching, based on a mathematical framework that represents all rotations of a template with a tensor field. Contrary to standard template matching, the computational complexity of the presented algorithm is independent of the rotation accuracy.

Using both, synthetic and real data from cryo-electron tomography, we demonstrate that tensorial template matching is much faster than template matching and has the potential to improve its accuracy.