Unsupervised dimensionality reduction of polarimetric data for pixel-wise pathological tissue differentiation

Extracellular matrix (ECM) constitutes a key basement structure to human organisms by acting as a complex network of large proteins and carbohydrates that provide structural support to surrounding cells. Remodeling in the extracellular matrix's structural fibers leads to insight into the development of diseases such as cancer, fibrosis and carcinoma. While standard tissues visualization in the ECM involves multiple lengthy histopathological staining protocols, Mueller matrix-based polarimetry provides label-free tissue slices' microstructural information and optical properties. This work aims to identify three types of fiber tissues commonly found in the ECM of gastrointestinal tissue specimens by analyzing their polarization properties. To address decomposition methods' reliance on restrictive hypotheses and inability with an individual polarization-based parameter to determine the nature of a given biological tissue; this study employs Uniform Manifold Approximation and Projection (UMAP) method to offer greater discriminative power and flexibility. Subsequently, polarization-based features will be extracted and compared between fiber regions statistically to discern potential diagnostic differences. By providing colorized images, this work aims to demonstrate the feasibility of distinguishing different fibers with polarization approach, offering insights for future clinical development while complementing existing staining methods for pathological tissue specimens.