Model-Based Approach for the Semi-Automatic Analysis of Collagen Birefringence in Polarized Light Microscopy

Abstract

Collagen is a key determinant of physio-pathological processes in different tissues. Polarization light microscopy (PLM) of histological sections is the gold-standard for birefringence-based collagen quantification, but post-session image analysis can be time-consuming and subjective. We propose an efficient semi-automatic computational approach for the quantification of collagen content from the analysis of PLM images of birefringent histological sections. The method is based on a physical model of light-sample interaction and birefringence effect production. It combines the information of bright and dark-field PLM images to segment the luminal region and detect the birefringent signal associated with collagen in the tissue region. User input is limited to the selection of a threshold on an image subset and the supervision of the processing, enabling fast analysis of large datasets. Modeling of the birefringence signal compensates for variability factors related to sample processing and image acquisition, such as section thickness variability and nonuniform illumination and transmittance. As a proof-of-concept, the method was applied to human cardiac tissue PLM images, acquired in 14 cardiac surgery patients with different arrhythmic profiles. The method was able to detect a significantly larger amount and higher heterogeneity of fibrosis in the atrium of patients with as opposed to without atrial fibrillation (p < 0.05). The proposed method can be a valid aid to quicken and reinforce the analysis of large sets of PLM images for the quantification of collagen distribution in different tissues and pathologies.