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DeepHCS: Bright-Field to Fluorescence Microscopy Image Conversion Using Deep Learning for Label-Free High-Content Screening

  • Gyuhyun Lee
  • Jeong-Woo Oh
  • Mi-Sun Kang
  • Nam-Gu Her
  • Myoung-Hee Kim
  • Won-Ki JeongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

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.

Notes

Acknowledgements

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gyuhyun Lee
    • 1
  • Jeong-Woo Oh
    • 2
  • Mi-Sun Kang
    • 4
  • Nam-Gu Her
    • 3
  • Myoung-Hee Kim
    • 4
  • Won-Ki Jeong
    • 1
    Email author
  1. 1.School of Electrical and Computer EngineeringUlsan National Institute of Science and Technology (UNIST)UlsanKorea
  2. 2.Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science and TechnologySungkyunkwan UniversitySeoulKorea
  3. 3.Samsung Medical Center (SMC)SeoulKorea
  4. 4.Department of Computer Science and EngineeringEwha Womans UniversitySeoulKorea

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