Live-cell analysis framework for quantitative phase imaging with slightly off-axis digital holographic microscopy

Abstract

Label-free quantitative phase imaging is an essential tool for studying in vitro living cells in various research fields of life sciences. Digital holographic microscopy (DHM) is a non-destructive full-field microscopy technique that provides phase images by directly measuring the optical path differences, which facilitates cell segmentation and allows the determination of several important cellular physical features, such as dry mass. In this work, we present a systematic analysis framework for live-cell imaging and morphological characterization, terms as LAF (live-cell analysis framework). All image processing algorithms involved in this framework are implemented on the high-resolution artifact-free quantitative phase images obtained by our previously proposed slightly off-axis holographic system (FPDH) and associated reconstruction methods. A highly robust automated cell segmentation method is applied to extract the valid cellular region, followed by live-cell analysis framework algorithms to determine the physical and morphological properties, including the area, perimeter, irregularity, volume and dry mass, of each individual cell. Experiments on live HeLa cells demonstrate the validity and effectiveness of the presented framework, revealing its potential for diverse biomedical applications.

Machine-learning-based classification of co-cultured cells from the phase images in digital holographic microscopy

“…The numerical reconstruction of hologram enables simultaneous amplitude and quantitative phase imaging. The phase maps reveal subtle changes in cell morphology, 1 protein content, 2 and variation in the refractive index, 3 offering i nvaluable i nsights i nto c ellular p rocesses. D HM h as a lso b een e xtensively u sed f or c ell classification using machine learning (ML) techniques.…”