Presentation
16 March 2023 Evaluation of cell dynamic activity using machine learning and intracellular migration observations (Conference Presentation)
Soongho Park, Thien Nguyen, Vinay Veluvolu, Jinho Park, Amir Gandjbakhche
Author Affiliations +
Abstract
We introduce cell dynamic activity analysis method-based combination of dynamic full-field optical coherence tomography (DFFOCT) and machine learning (ML) models. DFFOCT can monitor intracellular migration label-free by capturing scatters movement inside of cells. Since ML builds classification criteria through learning a lot of data, based on the intracellular scatter migration observed through DFFOCT, it is possible to judge abnormal signs of cells regardless of changes in the external experimental environment. We compared the suggested analysis method and staining analysis method for the change of state of HeLa cells (including cell data) and verified the validity.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Soongho Park, Thien Nguyen, Vinay Veluvolu, Jinho Park, and Amir Gandjbakhche "Evaluation of cell dynamic activity using machine learning and intracellular migration observations (Conference Presentation)", Proc. SPIE PC12391, Label-free Biomedical Imaging and Sensing (LBIS) 2023, PC1239114 (16 March 2023); https://doi.org/10.1117/12.2660490
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KEYWORDS
Machine learning

Diagnostics

Organisms

Imaging systems

Luminescence

Mode conditioning cables

Optical coherence tomography

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