Deep Learning-based Automated Cell Viability Measurement in Tissue Scaffold using OCT

Meijie Shih; Jiaying Wang; Xiaojun Yu; Yu Gan

Deep Learning-based Automated Cell Viability Measurement in Tissue Scaffold using OCT

Meijie Shih, Jiaying Wang, Xiaojun Yu, Yu Gan

Department of Biomedical Engineering

Stevens Institute of Technology

Research output: Contribution to conference › Paper › peer-review

Abstract

We proposed a deep learning-based approach to analyze cell viability from tissue scaffold images obtained from optical coherence tomography. Experimental results demonstrated the distinct viability patterns between active and dead cells and 3D visualization of cell distribution.

Original language	English
State	Published - 2024
Event	Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics - Fort Lauderdale, United States Duration: 7 Apr 2024 → 10 Apr 2024

Conference

Conference	Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics
Country/Territory	United States
City	Fort Lauderdale
Period	7/04/24 → 10/04/24

Cite this

@conference{94158a57eb21447cbb1668a6f1b1cd7a,

title = "Deep Learning-based Automated Cell Viability Measurement in Tissue Scaffold using OCT",

abstract = "We proposed a deep learning-based approach to analyze cell viability from tissue scaffold images obtained from optical coherence tomography. Experimental results demonstrated the distinct viability patterns between active and dead cells and 3D visualization of cell distribution.",

author = "Meijie Shih and Jiaying Wang and Xiaojun Yu and Yu Gan",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s); Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics ; Conference date: 07-04-2024 Through 10-04-2024",

year = "2024",

language = "English",

}

Deep Learning-based Automated Cell Viability Measurement in Tissue Scaffold using OCT. / Shih, Meijie; Wang, Jiaying; Yu, Xiaojun et al.
2024. Paper presented at Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics, Fort Lauderdale, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Deep Learning-based Automated Cell Viability Measurement in Tissue Scaffold using OCT

AU - Shih, Meijie

AU - Wang, Jiaying

AU - Yu, Xiaojun

AU - Gan, Yu

PY - 2024

Y1 - 2024

N2 - We proposed a deep learning-based approach to analyze cell viability from tissue scaffold images obtained from optical coherence tomography. Experimental results demonstrated the distinct viability patterns between active and dead cells and 3D visualization of cell distribution.

AB - We proposed a deep learning-based approach to analyze cell viability from tissue scaffold images obtained from optical coherence tomography. Experimental results demonstrated the distinct viability patterns between active and dead cells and 3D visualization of cell distribution.

UR - http://www.scopus.com/inward/record.url?scp=85204902901&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85204902901&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85204902901

T2 - Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

Y2 - 7 April 2024 through 10 April 2024

ER -

Deep Learning-based Automated Cell Viability Measurement in Tissue Scaffold using OCT

Abstract

Conference

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