SHG imaging and analysis of collagen fiber/fibril architecture in human ovarian cancer using machine learning and emission directionality

Paul J. Campagnola; Melissa Champer; Emily Shelton; Vikas Singh

doi:10.1117/12.3001103

13 March 2024 SHG imaging and analysis of collagen fiber/fibril architecture in human ovarian cancer using machine learning and emission directionality

Paul J. Campagnola, Melissa Champer, Emily Shelton, Vikas Singh

Author Affiliations +

Proceedings Volume PC12847, Multiphoton Microscopy in the Biomedical Sciences XXIV; PC128470L (2024) https://doi.org/10.1117/12.3001103
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We have shown that diseases including cancers and fibroses have significant changes in the collagen fibril and fiber structure. We have used machine learning methods to classify normal and diseased tissues based on the fiber morphology in SHG images, however the important features remain unknown. Using the StyleGAN framework, we trained the latent space and used PCA for this determination. We found that curvature and density were the most important attributes in ovarian cancer. We previously demonstrated qualitative differences in the fibril size and spacing in several tissues. We now developed a more complete computational model of 3D phasematching. This in conjunction with wavelength- dependent measurements of the spatial emission pattern, will afford the extraction of average fibril size and packing. Collectively these determinations will be broadly applicable to identifying characteristic diagnostic targets in a wide range of diseases.

Conference Presentation

Citation Download Citation

Paul J. Campagnola, Melissa Champer, Emily Shelton, and Vikas Singh "SHG imaging and analysis of collagen fiber/fibril architecture in human ovarian cancer using machine learning and emission directionality", Proc. SPIE PC12847, Multiphoton Microscopy in the Biomedical Sciences XXIV, PC128470L (13 March 2024); https://doi.org/10.1117/12.3001103

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