安徽医科大学 a. 生物医学工程学院;b. 3D打印与组织工程中心 安徽省转化医学研究所,安徽 合肥 230032
CycleGAN-Based Virtual Quantitative Differential Phase Contrast Imaging for
Red Blood Cell Classification
WANG Taoa, PENG Taoa, JIANG Mengduoa, ZHANG Cana, ZHANG Kaixuana, LU Fengyaa, ZHONG Zhenshenga, ZHOU Jinhuaa,b
a. School of Biomedical Engineering; b. 3D-Printing and Tissue Engineering Center, Anhui Provincial Institute of Translational
Medicine, Anhui Medical University, Hefei Anhui 230032, China
Abstract:Objective Virtual quantitative differential phase contrast (V-qDPC) reconstruction is realized by deep learning technology,which improves the contrast and robustness of quantitative phase imaging, and provides a new idea for automatic classification
of unlabeled red blood cells. Methods By encoding LED lighting, bright field (BF) images and differential phase contrast (DPC)
images are obtained by programmable LED illumination, and quantitative differential phase contrast (qDPC) image can be obtained
by phase reconstruction. End-to-end mapping of BF images to qDPC images is completed by cycle-consistent generative adversarial
network (CycleGAN). Results Based on the V-qDPC image generated by CycleGAN, the quality of V-qDPC image was optimal
when the experimental parameters were λ =7 and β=0.5. Compared with optical reconstruction qDPC image had better robustness
and anti-noise ability; AlexNet, ResNet 50 and VggNet were used to compare the automatic classification of unlabeled red blood
cell morphology. The results showed that V-qDPC images had better classification performance than qDPC images.
Conclusion Compared with the traditional qDPC reconstruction based on several oblique lighting images, V-qDPC algorithm
has better phase image quality and robustness. It is suitable for realizing automatic cell classification with high precision and high
efficiency, while eliminating the imaging optical path and hardware support. It is expected to be applied in biomedical research.