Deep optical flow: The interpretation and forecast of cell movement using fluorescence imaging

Kyungsu Lee; Haeyun Lee; Jae Youn Hwang

doi:10.1117/12.2578158

5 March 2021 Deep optical flow: The interpretation and forecast of cell movement using fluorescence imaging

Kyungsu Lee, Haeyun Lee, Jae Youn Hwang

Proceedings Volume 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX; 116470O (2021) https://doi.org/10.1117/12.2578158
Event: SPIE BiOS, 2021, Online Only

Abstract

We propose a novel deep learning algorithm, denoted as Deep Optical Flow (DoF), capable of interpreting and predicting cell behaviors with high accuracy in time-lapse fluorescence images. DoF has dual pipeline networks, including 4D-Rank convolution operations. One classifies the behavior of induvial cells while generating Optical Flow for the cells, whereas another predicts the next few frames of cells. DoF was verified with our and public datasets for cell tracking, segmentation, and identification. The experimental results demonstrate that DoF outperformed other state-of-the-arts in the analysis of cell behaviors. Therefore, these suggested that DoF has the potentials to become a novel tool for a better understanding of cell behaviors.

Conference Presentation

Citation Download Citation

Kyungsu Lee, Haeyun Lee, and Jae Youn Hwang "Deep optical flow: The interpretation and forecast of cell movement using fluorescence imaging", Proc. SPIE 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX, 116470O (5 March 2021); https://doi.org/10.1117/12.2578158

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