A machine learning approach to transport categorization for vesicle tracking data analysis

Seohyun Lee; Hyuno Kim; Hideo Higuchi; Masatoshi Ishikawa

doi:10.1117/12.2576170

5 March 2021 A machine learning approach to transport categorization for vesicle tracking data analysis

Seohyun Lee, Hyuno Kim, Hideo Higuchi, Masatoshi Ishikawa

Proceedings Volume 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX; 116470W (2021) https://doi.org/10.1117/12.2576170
Event: SPIE BiOS, 2021, Online Only

Abstract

The movement of intracellular vesicle contains essential biomedical information, mediating drug delivery and virus transmission. However, due to the interaction between vesicles and cytoskeletal networks, the trajectories of vesicle transport are often too complicated to understand the details. Particularly, identifying active transport via cytoskeletal network from random motion requires time-consuming mathematical methods. In this paper, we propose a machine learning approach to categorize the vesicle transport into active transport and random movement, using the features computed from the vector analysis of 3D vesicle transport trajectories. This approach is expected to simplify the process for vesicle transport data analysis.

Conference Presentation

Citation Download Citation

Seohyun Lee, Hyuno Kim, Hideo Higuchi, and Masatoshi Ishikawa "A machine learning approach to transport categorization for vesicle tracking data analysis", Proc. SPIE 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX, 116470W (5 March 2021); https://doi.org/10.1117/12.2576170

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