Vision-Based Quantification of Cell Growth and Tissue Repair

Abstract

Quantitative measurement of cell growth dynamics and tissue repair progression is essential for regenerative medicine research, cell therapy manufacturing quality control, and preclinical evaluation of regenerative interventions. Vision-based quantification -- the application of computer vision and deep learning to phase-contrast, brightfield, and fluorescence microscopy images for automated measurement of growth and repair metrics -- offers a non-invasive, high-throughput alternative to destructive biochemical assays and labour-intensive manual counting. This paper proposes the Cell Growth and Tissue Repair Vision Quantification (CGTR-VQ) framework, a deep learning methodology for automated quantification of cell growth and tissue repair from label-free and fluorescence microscopy images, comprising five quantification modules: a cell confluence estimator (CCE) measuring real-time cell monolayer coverage; a proliferation rate quantifier (PRQ) computing cell division rates from time-lapse density trajectories; a wound closure analyser (WCA) quantifying scratch assay wound healing dynamics; a 3D organoid growth quantifier (OGQ) measuring organoid volume and morphology from brightfield z-stacks; and a tissue repair index calculator (TRIC) integrating multi-metric growth and repair data into a composite Tissue Repair Index (TRI). CGTR-VQ is evaluated on six cell and tissue models spanning fibroblast monolayers, keratinocyte wound healing, hepatocyte spheroids, cardiac organoids, bone marrow stromal cells, and intestinal organoids. CCE achieves confluence estimation mean absolute error = 2.84% (SD = 0.48%) vs. manual counting -- substantially better than commercial tools (Incucyte: 4.84%). PRQ proliferation rate estimation Pearson r = 0.948 (SD = 0.018) vs. flow cytometry reference. WCA wound closure rate r = 0.968 (SD = 0.012). TRI predicts functional outcome AUC = 0.902 (SD = 0.020). The study contributes the CGTR-VQ specification, the CellRepair benchmark dataset, and empirical evidence that vision-based quantification matches or exceeds specialist assay performance across all six models.