Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis

Fuhai Li, Xiaobo Zhou, Jinwen Ma, Stephen T C Wong

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cellcycle analysis software package, DCELLIQ, which is freely available.

Original languageEnglish (US)
Article number5175475
Pages (from-to)96-105
Number of pages10
JournalIEEE Transactions on Medical Imaging
Issue number1
StatePublished - Jan 2010


  • Anti-cancer drug screening
  • Cell cycle analysis
  • Segmentation and tracking
  • Time-lapse fluorescence microscopy

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software


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