DeepHCS: Bright-Field to Fluorescence Microscopy Image Conversion Using Deep Learning for Label-Free High-Content Screening
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.
This work was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1A09000841) and Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) funded by MSIT (NRF-2015M3A9A7029725).
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