English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Cell behavior analysis is a fundamental process in cell biology to obtain the correlation between many diseases and abnormal cell behavior. Moreover, accurate number estimation plays an important role for the construction of cell lineage trees. In this paper, a novel Gaussian ant colony algorithm, for clustering or spatial overlap cell state and number estimator, simultaneously, is proposed. We have introduced a novel definition of the Gaussian ant system borrowed from the concept of the multi-Bernoulli random finite set (RFS) in the way that it encourages ants searching for cell regions effectively. The existence probability of ant colonies is considered for the number and state estimation of cells. Through experiments on two real cell sequences, it is confirmed that our proposed algorithm could automatically track clustering cells in various scenarios and has enabled superior performance compared with other state-of-the-art approaches.

A Novel Gaussian Ant Colony Algorithm for Clustering Cell Tracking