Presentation
11 March 2020 Deep learning framework enables 3D label-free tracking of immunological synapse using optical diffraction tomography (Conference Presentation)
Author Affiliations +
Proceedings Volume 11249, Quantitative Phase Imaging VI; 112490Y (2020) https://doi.org/10.1117/12.2551132
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Rapid, label-free, volumetric, and automated assessment in microscopy is necessary to assess the dynamic interactions between lymphocytes and their targets through the immunological synapse (IS) and the relevant immunological functions. However, attempts to realize the automatic tracking of IS dynamics have been stymied by the limitations of imaging techniques and computational analysis methods. Here, we demonstrate the automatic three-dimensional IS tracking by combining optical diffraction tomography and deep-learning-based segmentation. The proposed approach enables quantitative spatiotemporal analyses of IS regarding morphological and biochemical parameters related to its protein densities, offering a novel complementary method to fluorescence microscopy for studies in immunology.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Moosung Lee, Young-Ho Lee, Jinyeop Song, Geon Kim, YoungJu Jo, HyunSeok Min, Chan Hyuk Kim, and YongKeun Park "Deep learning framework enables 3D label-free tracking of immunological synapse using optical diffraction tomography (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490Y (11 March 2020); https://doi.org/10.1117/12.2551132
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KEYWORDS
Diffraction

Optical tomography

Tomography

Optical tracking

Microscopy

3D image processing

Stereoscopy

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