Algorithm for the precise detection of single and cluster cells in microfluidic applications

Mathias Girault, Akihiro Hattori, Hyonchol Kim, Kenji Matsuura, Masao Odaka, Hideyuki Terazono, Kenji Yasuda

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Recent advances in imaging flow cytometry and microfluidic applications have led to the development of suitable mathematical algorithms capable of detecting and identifying targeted cells in images. In contrast to currently existing algorithms, we herein proposed the identification and reconstruction of cell edges based on original approaches that overcome frequent detection limitations such as halos, noise, and droplet boundaries in microfluidic applications. Reconstructed cells are then discriminated between single cells and clusters of round-shaped cells, and cell information such as the area and location of a cell in an image is output. Using this method, 76% of cells detected in an image had an error

Original languageEnglish
JournalCytometry. Part A : the journal of the International Society for Analytical Cytology
DOIs
Publication statusAccepted/In press - 2016
Externally publishedYes

Fingerprint

Microfluidics
Noise
Flow Cytometry

Keywords

  • Algorithm
  • Cell detection
  • Cell reconstruction
  • Droplet
  • Imaging processing
  • Microfluidic

ASJC Scopus subject areas

  • Cell Biology
  • Histology
  • Pathology and Forensic Medicine

Cite this

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abstract = "Recent advances in imaging flow cytometry and microfluidic applications have led to the development of suitable mathematical algorithms capable of detecting and identifying targeted cells in images. In contrast to currently existing algorithms, we herein proposed the identification and reconstruction of cell edges based on original approaches that overcome frequent detection limitations such as halos, noise, and droplet boundaries in microfluidic applications. Reconstructed cells are then discriminated between single cells and clusters of round-shaped cells, and cell information such as the area and location of a cell in an image is output. Using this method, 76{\%} of cells detected in an image had an error",
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AU - Girault, Mathias

AU - Hattori, Akihiro

AU - Kim, Hyonchol

AU - Matsuura, Kenji

AU - Odaka, Masao

AU - Terazono, Hideyuki

AU - Yasuda, Kenji

PY - 2016

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AB - Recent advances in imaging flow cytometry and microfluidic applications have led to the development of suitable mathematical algorithms capable of detecting and identifying targeted cells in images. In contrast to currently existing algorithms, we herein proposed the identification and reconstruction of cell edges based on original approaches that overcome frequent detection limitations such as halos, noise, and droplet boundaries in microfluidic applications. Reconstructed cells are then discriminated between single cells and clusters of round-shaped cells, and cell information such as the area and location of a cell in an image is output. Using this method, 76% of cells detected in an image had an error

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