Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression

Anupama Nair, Chun Yu Lin, Feng Chun Hsu, Ta Hsiang Wong, Shu Chun Chuang, Yi Shan Lin, Chung Hwan Chen*, Paul Campagnola, Chi Hsiang Lien*, Shean Jen Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Previously, the discrimination of collagen types I and II was successfully achieved using peptide pitch angle and anisotropic parameter methods. However, these methods require fitting polarization second harmonic generation (SHG) pixel-wise information into generic mathematical models, revealing inconsistencies in categorizing collagen type I and II blend hydrogels. 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.

Original languageEnglish
Article number19534
JournalScientific reports
Issue number1
StatePublished - Dec 2023


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