Direct cell counting using macro-scale smartphone images of cell aggregates

Chikahiro Imashiro, Yuta Tokuoka, Kaito Kikuhara, Takahiro G. Yamada, Kenjiro Takemura, Akira Funahashi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


The field of bioengineering depends on technologies for stable cell culture. Conventionally, every process involved in cell culture has been performed manually, so the culture efficiency and stability can vary between trials or depending on the technician. Among these processes, cell counting is particularly important because cell density affects cell function. Conventional cell counting techniques for cell number estimation are inefficient and unstable because they involve the manual work of collecting a sample of the cell suspension. Thus, a cell counting method that is not susceptible to human error is needed. In this study, we present a novel cell counting method based on smartphone imaging and convolutional neural network-based image processing. Cells are aggregated by centrifuging in a tube and then imaged using a smartphone. The image is transferred to a server, and the cell number is predicted using convolutional neural networks on the server. All processes are performed by a custom-developed smartphone-compatible web app. Compared with the conventional method using a hemocytometer, our method yields more stable cell counting. Furthermore, the time and labor required for cell counting are significantly reduced. Our new method could potentially replace conventional cell counting techniques and thus enhance the stability and efficiency of bioengineering studies that require cell culture.

Original languageEnglish
Pages (from-to)170033-170043
Number of pages11
JournalIEEE Access
Publication statusPublished - 2020


  • Cell counting
  • Cell culture process
  • Computational biology
  • Convolutional neural network
  • Machine learning
  • Regression analysis
  • Supervised learning

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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