A Joint Tracking Approach via Ant Colony Evolution for Quantitative Cell Cycle Analysis

Benlian Xu, Mingli Lu, Jian Shi, Jinliang Cong, Brett Nener

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In this paper, we use an ant colony heuristic method to tackle the integration of data association and state estimation in the presence of cell mitosis, morphological change and uncertainty of measurement. Our approach first models the scouting behavior of an unlabeled ant colony as a chaotic process to generate a set of cell candidates in the current frame, then a labeled ant colony foraging process is modeled to construct an interframe matching between previously estimated cell states and current cell candidates through minimizing the optimal sub-pattern assignment metric for track (OSPA-T). The states of cells in the current frame are finally estimated using labeled ant colonies via a multi-Bernoulli parameter set approximated by individual food pheromone fields and heuristic information within the same region of support, the resulting trail pheromone fields over frames constitutes the cell lineage trees of the tracks. A four-stage track recovery strategy is proposed to monitor the history of all established tracks to reconstruct broken tracks in a computationally economic way. The labeling method used in this work is an improvement on previous techniques. The method has been evaluated on publicly available, challenging cell image sequences, and a satisfied performance improvement is achieved in contrast to the state-of-the-art methods.

Original languageEnglish
Article number9233941
Pages (from-to)2338-2349
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number6
Publication statusPublished - Jun 2021


Dive into the research topics of 'A Joint Tracking Approach via Ant Colony Evolution for Quantitative Cell Cycle Analysis'. Together they form a unique fingerprint.

Cite this