Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression

Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark Anastasio

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

2 Scopus citations

Abstract

Accurate cell counting in microscopic images is important for medical diagnoses and biological studies. However, manual cell counting is very time-consuming, tedious, and prone to subjective errors. We propose a new density regression-based method for automatic cell counting that reduces the need to manually annotate experimental images. A supervised learning-based density regression model (DRM) is trained with annotated synthetic images (the source domain) and their corresponding ground truth density maps. A domain adaptation model (DAM) is built to map experimental images (the target domain) to the feature space of the source domain. By use of the unsupervised learning-based DAM and supervised learning-based DRM, a cell density map of a given target image can be estimated, from which the number of cells can be counted. Results from experimental immunofluorescent microscopic images of human embryonic stem cells demonstrate the promising performance of the proposed counting method.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
ISBN (Electronic)9781510625594
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Digital Pathology - San Diego, United States
Duration: Feb 20 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10956
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Digital Pathology
CountryUnited States
CitySan Diego
Period02/20/1902/21/19

Keywords

  • Automatic cell counting
  • Density regression
  • Domain adaptation
  • Microscopic images
  • Supervised learning
  • Unsupervised adversarial learning

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  • Cite this

    He, S., Minn, K. T., Solnica-Krezel, L., Li, H., & Anastasio, M. (2019). Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression. In J. E. Tomaszewski, & A. D. Ward (Eds.), Medical Imaging 2019: Digital Pathology [1095604] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10956). SPIE. https://doi.org/10.1117/12.2513058