Universal Convolutional Neural Network for Histology-Independent Analysis of Collagen Fiber Organization in Scar Tissue

Abstract

Histological examination of collagen fiber organization is essential for pathologists to observe the wound healing process. A convolutional neural network (CNN) can be utilized to visually analyze collagen fibers during tissue remodeling in histology images. In this study, a universal CNN (UCNN) independent of the histological staining process is proposed to classify the histology images of burn-induced scar tissues and characterize collagen fiber organization. Normal and scar tissues obtained from an in vivo rodent model are stained using Masson’s Trichrome (MT) and Hematoxylin & Eosin (H&E). The proposed universal model is trained using both MT- and H&E-stained histological image datasets over multiple scales with color augmentation, and classification accuracies of up to 98% and 97% are achieved for the MT- and H&E-stained image datasets, respectively. Regardless of the histological staining process used, the collagen characteristics are visualized by determining the density and directional variance of the normal and scar tissues by using the features extracted with the proposed universal model. Statistical analysis results demonstrated clear differences between scar and normal tissues in terms of collagen fiber organization. The proposed UCNN model can contribute to the development of an intelligent and efficient method that pathologists can use to rapidly evaluate wound healing and tissue remodeling.

Keywords

• Histology image
• collagen fiber characterization
• scar tissue classification
• convolutional neural network
• hue-saturation specificity analysis