3D Image Reconstruction Techniques for Regenerative Tissue Modeling

Abstract

Three-dimensional tissue modelling is increasingly essential for regenerative medicine, enabling analysis of spatial tissue organisation, vascular network geometry, and cellular distribution that are invisible to two-dimensional histological sections. Multiple imaging modalities generate 3D tissue data: micro-CT for bone, light-sheet fluorescence microscopy (LSFM) for cleared soft tissue and organoids, confocal z-stack for cultured cells, and MRI for clinical-scale assessment. This paper proposes the Regenerative Tissue 3D Reconstruction and Analysis (RT3RA) framework comprising five components: a multi-modal 3D segmentation network (M3SN); a tissue architecture quantifier (TAQ); a vascular network analyser (VNA); a 3D-to-2D projection quality assessor (3Q2A) quantifying information lost in 2D histological sampling; and a regeneration architecture predictor (RAP). RT3RA is evaluated on eight 3D tissue datasets across four imaging modalities from six regenerative models. M3SN achieves Dice = 0.884 (SD = 0.028), outperforming 3D U-Net (0.824) and standard nnU-Net (0.848). VNA reconstructs vascular networks with 94.2% vessel segment recall. 3Q2A reveals that 2D sections systematically underestimate angiogenic vessel density by 28.4%. RAP predicts regeneration quality with AUC = 0.912 -- a 12.8pp improvement over 2D section-based prediction. The study contributes the RT3RA specification, the RegenVolume benchmark, and empirical evidence that 3D assessment substantially improves regeneration quantification accuracy over standard 2D histology.