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Proceedings Paper

Deep learning framework enables 3D label-free tracking of immunological synapse using optical diffraction tomography (Conference Presentation)

Paper 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.

Paper Details

Date Published: 11 March 2020
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Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490Y (11 March 2020);
Show Author Affiliations
Moosung Lee, KAIST (Korea, Republic of)
Young-Ho Lee, KAIST (Korea, Republic of)
Jinyeop Song, KAIST (Korea, Republic of)
Geon Kim, KAIST (Korea, Republic of)
YoungJu Jo, KAIST (Korea, Republic of)
HyunSeok Min, Tomocube, Inc. (Korea, Republic of)
Chan Hyuk Kim, KAIST (Korea, Republic of)
YongKeun Park, KAIST (Korea, Republic of)


Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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