Quantitative Spatial Analysis of Matrix Remodelling in Wounds Using Digital Pathology and Fractal Analysis
Wound healing involves spatially heterogeneous changes in matrix composition and organisation that can be difficult to capture using qualitative histological assessment alone. We developed a reproducible digital pathology workflow to quantify matrix features in cutaneous wound sections, compare them with unwounded skin, and derive an exploratory Histological Wound Index (HWI). Four normal-skin and six wound sections were analysed using QuPath, ImageJ/FracLac and Python. Approximately 500 x 500 micrometer regions of interest (ROI) were assigned to normal skin, wound centre, proximal edge or distant edge. Collagen, mucin and necrotic tissue coverage were quantified using image classifiers and analysed with linear mixed-effects models. Collagen architecture was assessed from collagen-selective binary masks using fractal dimension and lacunarity. Collagen coverage, fractal dimension, mucin coverage, necrosis and lacunarity were z-standardised and integrated into the HWI. Unwounded skin showed high collagen coverage and fractal dimension, with low lacunarity, mucin and necrosis. In contrast, wound-centre tissue showed low collagen coverage and fractal dimension together with higher mucin and necrosis. Distant-edge regions retained comparatively normal-like characteristics, whereas proximal-edge regions showed intermediate values. HWI was highest in normal skin and lowest in the wound centre. A descriptive matched-ROI comparison of two wound sections reproduced this regional ordering when all component measures were derived from identical ROI boundaries. This workflow provides a reproducible framework for quantifying regional ECM features and collagen-mask architecture in wound histology. The HWI is an exploratory histological summary requiring validation in larger longitudinal datasets.