Automated cell properties toolbox from 3D bioprinted hydrogel scaffolds via deep learning and optical coherence tomography.

Abstract

Accurately assessing cell viability and morphological properties within 3D bioprinted hydrogel scaffolds is essential for tissue engineering but remains challenging due to the limitations of existing invasive and threshold-based methods. We present a computational toolbox that automates cell viability analysis and quantifies key properties such as elongation, flatness, and surface roughness. This framework integrates optical coherence tomography (OCT) with deep learning-based segmentation, achieving a mean segmentation precision of 88.96%. By leveraging OCT's high-resolution imaging with deep learning-based segmentation, our novel approach enables non-invasive, quantitative analysis, which can advance rapid monitoring of 3D cell cultures for regenerative medicine and biomaterial research.