Tracking of biological cells is necessary to improve the understanding of their growth and behavior. Most methods used in cell tracking are time consuming and inaccurate for large population density or closely interacting cells. In this paper, a fast and accurate ant-inspired estimating algorithm for tracking multiple cells is proposed that uses a dual prediction mechanism and a pheromone updating strategy. The dual prediction mechanism is novel and uses ant individual state prediction for a given colony as well as the corresponding pheromone field prediction from the previous frame to the current frame. Ant state prediction aims to guide ant clustering around cells of interest, while pheromone field prediction helps to accelerate the pheromone formation of the current frame using the Gaussian mixture model (GMM) approximation technique. To handle the problem of tracking closely interacting cells, we design a novel ant decision-making model based on the pheromone gradient information and heuristic function with two forms. The pheromone updating strategy is also a novel pheromone diffusion and deposit model to obtain the expected pheromone field for extracting cell states in collision and cohesion. We provide quantitative validation of the method using two challenging datasets characterized by cohesion and collision by comparing them with the results from recently reported approaches.
|Number of pages||12|
|Journal||Engineering Applications of Artificial Intelligence|
|Publication status||Published - 1 Jun 2018|