Paper 13139-40
Paper 13139-40
Categorization of collagen hydrogels using multipolarization SHG imaging with deep learning
18 August 2024 • 5:30 PM - 5:45 PM PDT | Conv. Ctr. Room 11B
Abstract
In this study, a ResNet approach based on multipolarization SHG imaging is proposed for the categorization and regression of collagen type I and II blend hydrogels at 0%, 25%, 50%, 75%, and 100% type II, without the need for
prior time-consuming model fitting. A ResNet model, pretrained on 18 progressive polarization SHG images at 10° intervals for each percentage, categorizes the five blended collagen hydrogels with a mean absolute error (MAE) of 0.021, while the model pretrained on nonpolarization images exhibited 0.083 MAE. Moreover, the pretrained models can also generally regress the blend hydrogels at 20%, 40%, 60%, and 80% type II. In conclusion, the multipolarization SHG image-based ResNet analysis demonstrates the potential for an automated approach using deep learning to extract valuable information from the collagen matrix.
Presenter
National United Univ. (Taiwan)
Chi-Hsiang Lien is an assistant professor of Mechanical Engineering in National United University and received his B.S. degree in Mechanical Engineering from National United University in 2008 and his Ph.D degree in Engineering Science from National Cheng Kung University in 2014. He was invited to work with Prof. Paul J. Campagnola in UW-Madison from Jan. 2012 to July 2013. He is the author of more than 20 journal papers and has written about Mueller polarimetry, temporal focusing microscopy techniques, and polarization second harmonic generation imaging and analysis techniques for collagen. His current research interests include robot arm applications, nonlinear optics in biological microscopy and polarization optics for biomedical applications. He is a member of SPIE