TY - GEN
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
N1 - Publisher Copyright:
© 2024 The Author (s).
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=85211726368&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85211726368&partnerID=8YFLogxK
U2 - 10.1364/translational.2024.jm4a.12
DO - 10.1364/translational.2024.jm4a.12
M3 - Conference contribution
AN - SCOPUS:85211695307
T3 - Optics and the Brain, BRAIN 2024 in Proceedings Optica Biophotonics Congress: Biomedical Optics 2024, Translational, Microscopy, OCT, OTS, BRAIN - Part of Optica Biophotonics Congress: Biomedical Optics
BT - Optics and the Brain, BRAIN 2024 in Proceedings Optica Biophotonics Congress
T2 - Optics and the Brain, BRAIN 2024 - Part of Optica Biophotonics Congress: Biomedical Optics
Y2 - 7 April 2024 through 10 April 2024
ER -