Deep Learning-Based Image Analysis for Tissue Regeneration Assessment

Abstract

Histological image analysis -- the quantitative assessment of tissue architecture, cellular composition, and structural repair from stained tissue sections -- is the gold standard for evaluating regenerative medicine outcomes in preclinical and clinical studies. Manual histological assessment by pathologists is time-intensive, subjective, and poorly scalable for the large cohort sizes and multi-timepoint study designs required by regenerative medicine clinical trials. Deep learning-based computational pathology offers automated, quantitative, and reproducible tissue assessment at scale, but existing computational pathology tools have been primarily developed for oncology applications and lack the regeneration-specific tissue features -- fibrosis grade, neovascularisation extent, stem cell niche integrity, extracellular matrix organisation -- required for regenerative medicine assessment. This paper proposes the Tissue Regeneration Image Analysis (TRIA) framework, a deep learning methodology for automated quantitative assessment of tissue regeneration from histological images, comprising four image analysis components: a regeneration feature extractor (RFE) that identifies regeneration-relevant cellular and structural features from H&E-stained; tissue sections; a fibrosis and ECM quantifier (FEQ) that quantifies collagen deposition, fibrosis grade, and ECM organisation from Masson's trichrome and picrosirius red-stained sections; a vascularisation and cellularity assessor (VCA) that quantifies neovascularisation and cell density from CD31-stained and DAPI sections; and a regeneration outcome predictor (ROP) that integrates multi-stain image features into a quantitative Tissue Regeneration Score (TRS). TRIA is evaluated on a histological image dataset of 4,284 tissue sections from six regenerative medicine models (skeletal muscle, cardiac, liver, cartilage, bone, and neural), encompassing H&E;, Masson's trichrome, picrosirius red, CD31 immunostaining, and DAPI staining across 1,264 individual animals and patients. TRIA RFE achieves feature extraction accuracy of 0.924 mAP (SD = 0.028) for regeneration-relevant histological features versus expert pathologist annotation. FEQ fibrosis grading agrees with expert consensus in 91.4% (SD = 2.8%) of sections -- exceeding inter-expert agreement (84.6%). ROP predicts functional recovery outcomes from histological images with AUC = 0.888 (SD = 0.024). The study contributes the TRIA specification, the RegenHisto dataset of 4,284 annotated regenerative tissue sections, and the TRS outcome prediction score.