DeepHCS: Bright-field to fluorescence microscopy image conversion using deep learning for label-free high-content screening

Abstract

In this paper, we propose a novel image processing method, DeepHCS, to transform bright-field microscopy images into synthetic fluorescence images of cell nuclei biomarkers commonly used in high-content drug screening. The main motivation of the proposed work is to automatically generate virtual biomarker images from conventional bright-field images, which can greatly reduce time-consuming and laborious tissue preparation efforts and improve the throughput of the screening process. DeepHCS uses bright-field images and their corresponding cell nuclei staining (DAPI) fluorescence images as a set of image pairs to train a series of end-to-end deep convolutional neural networks. By leveraging a state-of-the-art deep learning method, the proposed method can produce synthetic fluorescence images comparable to real DAPI images with high accuracy. We demonstrate the efficacy of this method using a real glioblastoma drug screening dataset with various quality metrics, including PSNR, SSIM, cell viability correlation (CVC), the area under the curve (AUC), and the IC50.