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Label-free three-dimensional analyses of live cells with deep-learning-based segmentation exploiting refractive index distributions

Jinho Choi, Hye-Jin Kim, Gyuhyeon Sim, Sumin Lee, Wei Sun Park, Jun Hyung Park, Ha-Young Kang, Moosung Lee, Won Do Heo, Jaegul Choo, Hyunseok Min, YongKeun Park
doi: https://doi.org/10.1101/2021.05.23.445351
Jinho Choi
1Tomocube Inc., Daejeon 34051, Republic of Korea
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Hye-Jin Kim
1Tomocube Inc., Daejeon 34051, Republic of Korea
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Gyuhyeon Sim
4Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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Sumin Lee
1Tomocube Inc., Daejeon 34051, Republic of Korea
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Wei Sun Park
2Department of Biological Sciences, KAIST, Daejeon 34141, Republic of Korea
4Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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Jun Hyung Park
1Tomocube Inc., Daejeon 34051, Republic of Korea
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Ha-Young Kang
1Tomocube Inc., Daejeon 34051, Republic of Korea
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Moosung Lee
4Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
5KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
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Won Do Heo
2Department of Biological Sciences, KAIST, Daejeon 34141, Republic of Korea
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Jaegul Choo
3Graduate School of Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea
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  • For correspondence: jchoo@kaist.ac.kr hsmin@tomocube.com yk.park@kaist.ac.kr
Hyunseok Min
1Tomocube Inc., Daejeon 34051, Republic of Korea
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  • For correspondence: jchoo@kaist.ac.kr hsmin@tomocube.com yk.park@kaist.ac.kr
YongKeun Park
4Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
5KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
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  • For correspondence: jchoo@kaist.ac.kr hsmin@tomocube.com yk.park@kaist.ac.kr
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Abstract

Visualisations and analyses of cellular and subcellular organelles in biological cells is crucial for the study of cell biology. However, existing imaging methods require the use of exogenous labelling agents, which prevents the long-time assessments of live cells in their native states. Here we propose and experimentally demonstrate three-dimensional segmentation of subcellular organelles in unlabelled live cells, exploiting a 3D U-Net-based architecture. We present the high-precision three-dimensional segmentation of cell membrane, nucleus membrane, nucleoli, and lipid droplets of various cell types. Time-lapse analyses of dynamics of activated immune cells are also analysed using label-free segmentation.

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Competing Interest Statement

J. Choi, H.-J. Kim, S. Lee, W.-S. Park, J. Park, H, Kang, and Y.K. Park have financial interests in Tomocube Inc., a company that commercialises optical diffraction tomogram, and quantitative phase imaging instruments and is one of the sponsors of the work.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 23, 2021.
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Label-free three-dimensional analyses of live cells with deep-learning-based segmentation exploiting refractive index distributions
Jinho Choi, Hye-Jin Kim, Gyuhyeon Sim, Sumin Lee, Wei Sun Park, Jun Hyung Park, Ha-Young Kang, Moosung Lee, Won Do Heo, Jaegul Choo, Hyunseok Min, YongKeun Park
bioRxiv 2021.05.23.445351; doi: https://doi.org/10.1101/2021.05.23.445351
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Label-free three-dimensional analyses of live cells with deep-learning-based segmentation exploiting refractive index distributions
Jinho Choi, Hye-Jin Kim, Gyuhyeon Sim, Sumin Lee, Wei Sun Park, Jun Hyung Park, Ha-Young Kang, Moosung Lee, Won Do Heo, Jaegul Choo, Hyunseok Min, YongKeun Park
bioRxiv 2021.05.23.445351; doi: https://doi.org/10.1101/2021.05.23.445351

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