Categorization of collagen hydrogels using multipolarization SHG imaging with deep learning

Chi-Hsiang Lien; Chung-Hwan Chen; Anupama Nair; Chun-Yu Lin; Shu-Chun Chuang; Shean-Jen Chen

doi:10.1117/12.3032685

3 October 2024 Categorization of collagen hydrogels using multipolarization SHG imaging with deep learning

Chi-Hsiang Lien, Chung-Hwan Chen, Anupama Nair, Chun-Yu Lin, Shu-Chun Chuang, Shean-Jen Chen

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Proceedings Volume 13139, Ultrafast Nonlinear Imaging and Spectroscopy XII; 131390F (2024) https://doi.org/10.1117/12.3032685
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

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.

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Citation Download Citation

Chi-Hsiang Lien, Chung-Hwan Chen, Anupama Nair, Chun-Yu Lin, Shu-Chun Chuang, and Shean-Jen Chen "Categorization of collagen hydrogels using multipolarization SHG imaging with deep learning", Proc. SPIE 13139, Ultrafast Nonlinear Imaging and Spectroscopy XII, 131390F (3 October 2024); https://doi.org/10.1117/12.3032685

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KEYWORDS

Collagen

Second harmonic generation

Hydrogels

Deep learning

Polarization

Matrices

Microscopy

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