Simultaneous quantification of dynamic bacterial deformation and motility by machine learning

Authors

Takabe, K., Ugawa, S., Koizumi, N., Nakamura, S.

Abstract

We developed a convolutional neural network-based machine learning technique to simultaneously analyze the morphology and motility of spirochetal bacteria swimming with continuous cellular deformation. Matching probabilities between experimental images and learned models realizes quantification of cell morphology and association with motility. This method can be applied to diverse transformable cells, offering critical biophysical insights into microbial dynamics.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 09 Jul 2026.

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