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

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.