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)


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).


  1. 1.
    Ali, R., Gooding, M., Szilágyi, T., Vojnovic, B., Christlieb, M., Brady, M.: Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images. Mach. Vis. Appl. 23(4), 607–621 (2012)CrossRefGoogle Scholar
  2. 2.
    Boutros, M., Heigwer, F., Laufer, C.: Microscopy-based high-content screening. Cell 163(6), 1314–1325 (2015)CrossRefGoogle Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  4. 4.
    Darzynkiewicz, Z., Li, X., Gong, J.: Assays of cell viability: discrimination of cells dying by apoptosis. Methods Cell Biol. 41, 15–38 (1994)CrossRefGoogle Scholar
  5. 5.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016)
  6. 6.
    Liimatainen, K., Ruusuvuori, P., Latonen, L., Huttunen, H.: Supervised method for cell counting from bright field focus stacks. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 391–394. IEEE (2016)Google Scholar
  7. 7.
    Quan, T.M., Hilderbrand, D.G., Jeong, W.K.: FusionNet: a deep fully residual convolutional neural network for image segmentation in connectomics. arXiv preprint arXiv:1612.05360 (2016)
  8. 8.
    Quartararo, C.E., Reznik, E., DeCarvalho, A.C., Mikkelsen, T., Stockwell, B.R.: High-throughput screening of patient-derived cultures reveals potential for precision medicine in glioblastoma. ACS Med. Chem. Lett. 6(8), 948–952 (2015)CrossRefGoogle Scholar
  9. 9.
    Selinummi, J., et al.: Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images. PloS One 4(10), e7497 (2009)CrossRefGoogle Scholar
  10. 10.
    Tikkanen, T., Ruusuvuori, P., Latonen, L., Huttunen, H.: Training based cell detection from bright-field microscope images. In: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 160–164. IEEE (2015)Google Scholar

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

Personalised recommendations