Discrimination and quantification of scar tissue by Mueller matrix imaging with machine learning

Abstract

Scarring is one of the biggest areas of unmet need in the long-term success of glaucoma filtration surgery. Quantitative evaluation of the scar tissue and the post-operative structure with micron scale resolution facilitates development of anti-fibrosis techniques. However, the distinguishment of conjunctiva, sclera and the scar tissue in the surgical area still relies on pathologists’ experience. Since polarized light imaging is sensitive to anisotropic properties of the media, it is ideal for discrimination of scar in the subconjunctival and episcleral area by characterizing small differences between proportion, organization and the orientation of the fibers. In this paper, we defined the conjunctiva, sclera, and the scar tissue as three target tissues after glaucoma filtration surgery and obtained their polarization characteristics from the tissue sections by a Mueller matrix microscope. Discrimination score based on parameters derived from Mueller matrix and machine learning was calculated and tested as a diagnostic index. As a result, the discrimination score of three target tissues showed significant difference between each other ([Formula: see text]). The visualization of the discrimination results showed significant contrast between target tissues. This study proved that Mueller matrix imaging is effective in ocular scar discrimination and paves the way for its application on other forms of ocular fibrosis as a substitute or supplementary for clinical practice.

Keywords

• Tissue discrimination
• glaucoma filtration surgery
• polarized light
• Mueller matrix
• machine learning