Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.

Abstract

Digital holographic microscopy as a non-contacting, non-invasive, and highly accurate measurement technology, is becoming a valuable method for quantitatively investigating cells and tissues. Reconstruction of phases from a digital hologram is a key step in quantitative phase imaging for biological and biomedical research. This study proposes a two-stage deep convolutional neural network named VY-Net, to realize the effective and robust phase reconstruction of living red blood cells. The VY-Net can obtain the phase information of an object directly from a single-shot off-axis digital hologram. We also propose two new indices to evaluate the reconstructed phases. In experiments, the mean of the structural similarity index of reconstructed phases can reach 0.9309, and the mean of the accuracy of reconstructions of reconstructed phases is as high as 91.54%. An unseen phase map of a living human white blood cell is successfully reconstructed by the trained VY-Net, demonstrating its strong generality.