Deep learning for tracking of intracellular vesicles in time-lapse microscopy images

Zhichao Liu; Luhong Jin; Alexander Nedzved; Sergey Ablameyko; Yingke Xu

doi:10.1117/12.2604449

24 November 2021 Deep learning for tracking of intracellular vesicles in time-lapse microscopy images

Zhichao Liu, Luhong Jin, Alexander Nedzved, Sergey Ablameyko, Yingke Xu

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 1206907 (2021) https://doi.org/10.1117/12.2604449
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

Advanced microscopy techniques have opened new opportunities for biomedical research. Fluorescence microscopy enables researchers to observe subcellular structures with specific labeling. Quantitatively measuring the dynamics of intracellular objects is essential to understand the underlying regulatory mechanism. Protein-containing vesicles in cell are involved in various biological processes, such as material transportation, organelle interaction and hormonal regulation, whose dynamic characteristics are significant to disease diagnosis and drug screening. Although there have been some algorithms developed for vesicle tracking, most of them have limited performance when dealing with images with low resolution, poor signal-to-noise ratio (SNR) and complicated motion. In this article, we proposed a deep learning-based method for intracellular vesicle tracking. We trained the U-Net for vesicle localization and motion classification on the simulated datasets, which demonstrated high accuracy. We profoundly improved the performance of particle tracking using motion classification, and quantified the dynamic characteristics of intracellular vesicles according to the tracking results with satisfying outcomes. We anticipate that this novel method would have vast applications in analyzing the dynamics in living cell.

Citation Download Citation

Zhichao Liu, Luhong Jin, Alexander Nedzved, Sergey Ablameyko, and Yingke Xu "Deep learning for tracking of intracellular vesicles in time-lapse microscopy images", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 1206907 (24 November 2021); https://doi.org/10.1117/12.2604449

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KEYWORDS

Microscopy

Computer simulations

Motion models

Network architectures

Data modeling

Image segmentation

Time lapse microscopy

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