Nuclei counting in microscopy images with three dimensional generative adversarial networks

Shuo Han, Soonam Lee, Chichen Fu, Paul Salama, Kenneth W. Dunn, Edward J. Delp

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Microscopy image analysis can provide substantial information for clinical study and understanding of biological structures. Two-photon microscopy is a type of fluorescence microscopy that can image deep into tissue with near-infrared excitation light. We are interested in methods that can detect and characterize nuclei in 3D fluorescence microscopy image volumes. In general, several challenges exist for counting nuclei in 3D image volumes. These include "crowding" and touching of nuclei, overlapping of nuclei, and shape and size variances of the nuclei. In this paper, a 3D nuclei counter using two different generative adversarial networks (GAN) is proposed and evaluated. Synthetic data that resembles real microscopy image is generated with a GAN and used to train another 3D GAN that counts the number of nuclei. Our approach is evaluated with respect to the number of groundtruth nuclei and compared with common ways of counting used in the biological research. Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The accuracy results of proposed nuclei counter are compared with the ImageJ's 3D object counter (JACoP) and the 3D watershed. Both the counting accuracy and the object-based evaluation show that the proposed technique is successful for counting nuclei in 3D.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini, Bennett A. Landman
ISBN (Electronic)9781510625457
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Image Processing - San Diego, United States
Duration: Feb 19 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2019: Image Processing
CountryUnited States
CitySan Diego


  • fluorescence microscopy
  • generative adversarial networks
  • nuclei counting
  • synthetic data generation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Han, S., Lee, S., Fu, C., Salama, P., Dunn, K. W., & Delp, E. J. (2019). Nuclei counting in microscopy images with three dimensional generative adversarial networks. In E. D. Angelini, E. D. Angelini, E. D. Angelini, & B. A. Landman (Eds.), Medical Imaging 2019: Image Processing [109492Y] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10949). SPIE.