Work

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

Year: 2025

Type: article

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.

Source: Biomedical Optics Express

DOI: 10.1364/boe.550401