Isolated Random Forest Assisted Spatio-Temporal Ant Colony Evolutionary Algorithm for Cell Tracking in Time-Lapse Sequences.

Multi-Object tracking in real world environments is a tough problem, especially for cell morphogenesis with division. Most cell tracking methods are hard to achieve reliable mitosis detection, efficient inter-frame matching, and accurate state estimation simultaneously within a unified tracking framework. In this paper, we propose a novel unified framework that leverages a spatio-temporal ant colony evolutionary algorithm to track cells amidst mitosis under measurement uncertainty. Each Bernoulli ant colony representing a migrating cell is able to capture the occurrence of mitosis through the proposed Isolation Random Forest (IRF)-assisted temporal mitosis detection algorithm with the assumption that mitotic cells exhibit unique spatio-temporal features different from non-mitotic ones. Guided by prediction of a division event, multiple ant colonies evolve between consecutive frames according to an augmented assignment matrix solved by the extended Hungarian method. To handle dense cell populations, an efficient group partition between cells and measurements is exploited, which enables multiple assignment tasks to be executed in parallel with a reduction in matrix dimension. After inter-frame traversing, the ant colony transitions to a foraging stage in which it begins approximating the Bernoulli parameter to estimate cell state by iteratively updating its pheromone field. Experiments on multi-cell tracking in the presence of cell mitosis and morphological changes are conducted, and the results demonstrate that the proposed method outperforms state-of-the-art approaches, striking a balance between accuracy and computational efficiency.

Visualization analysis of research hotspots on structural topology optimization based on CiteSpace.

Structural topology optimization has gained widespread attention due to more possibilities of innovative structural design. The current research focus/hotspots, application areas, main research scholars, institutions and the countries involved in structural topology optimization are visually presented through clustering and visual analysis based on CiteSpace. The four metric dimensions of the literatures in this paper are as follows: annual quantity of papers and core countries, core authors and co-authors' institutions, hotspots and burst terms, and the highly co-cited papers. The results show the research hotspots in this field are concentrated on keywords such as "level set method", "sensitivity analysis", "homogenization", "genetic algorithm", etc. Regarding the research frontier, "moving morphable component (MMC)", "additive manufacturing (AM)" and "deep learning" are hot topics. In addition, Y. Sui, Z. Kang and O. Sigmund, etc. have high publications. M. Bendsøe and O. Sigmund have high citations. Dalian University of Technology, Technical University of Denmark, etc. are prominent institutions. Moreover, China accounts for more than 34% in the terms of original WOS literatures following by the USA and Australia. This paper could identify structural topology optimization development patterns for the scholars concerned with this field, especially novices, to quickly focus and track the research priorities.

An Automated Cell Tracking Approach With Multi-Bernoulli Filtering and Ant Colony Labor Division.

In this article, we take as inspiration the labor division into scouts and workers in an ant colony and propose a novel approach for automated cell tracking in the framework of multi-Bernoulli random finite sets. To approximate the Bernoulli parameter sets, we first define an existence probability of an ant colony as well as its discrete density distribution. During foraging, the behavior of scouts is modeled as a chaotic movement to produce a set of potential candidates. Afterwards, a group of workers, i.e., a worker ant colony, is recruited for each candidate, which then embark on gathering heuristic information in a self-organized way. Finally, the pheromone field is formed by the corresponding worker ant colony, from which the Bernoulli parameter is derived and the state of the cell is estimated accordingly to be associated with the existing tracks. Performance comparisons with other previous approaches are conducted on both simulated and real cell image sequences and show the superiority of this algorithm.

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

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.

An Ant Colony Inspired Multi-Bernoulli Filter for Cell Tracking in Time-Lapse Microscopy Sequences.

The analysis of the dynamic behavior of cells in time-lapse microscopy sequences requires the development of reliable and automatic tracking methods capable of estimating individual cell states and delineating the lineage trees corresponding to the tracks. In this paper, we propose a novel approach, i.e., an ant colony inspired multi-Bernoulli filter, to handle the tracking of a collection of cells within which mitosis, morphological change and erratic dynamics occur. The proposed technique treats each ant colony as an independent one in an ant society, and the existence probability of an ant colony and its density distribution approximation are derived from the individual pheromone field and the corresponding heuristic information for the approximation to the multi-Bernoulli parameters. To effectively guide ant foraging between consecutive frames, a dual prediction mechanism is proposed for the ant colony and its pheromone field. The algorithm performance is tested on challenging datasets with varying population density, frequent cell mitosis and uneven motion over time, demonstrating that the algorithm outperforms recently reported approaches.

A Real-Time Analysis Method for Pulse Rate Variability Based on Improved Basic Scale Entropy.

Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time consumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV) signal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining BSEA and sliding window iterative theory. The blood pressure signals of healthy young and old subjects are chosen from the authoritative international database MIT/PhysioNet/Fantasia to generate PRV signals as the experimental data. Then, the BSEA and the SWIBSEA are used to analyze the experimental data; the results show that the SWIBSEA reduces the time consumption and the buffer cache space while it gets the same entropy as BSEA. Meanwhile, the changes of base scale entropy (BSE) for healthy young and old subjects are the same as that of HRV signal. Therefore, the SWIBSEA can be used for deriving some information from long-term and short-term PRV signals in real time, which has the potential for dynamic PRV signal analysis in some portable and wearable medical devices.

False alarm suppression of multipulsed laser ranging system with Geiger-mode detector.

The false alarm probability is of great concern when designing and evaluating the performance of a multipulsed laser ranging system with a Geiger-mode avalanche photodiode. In this paper, based on the statistical distribution difference of the arrival time of the echo photons and noise in the time histogram, a false alarm suppression algorithm is presented. According to the data-processing method of the algorithm, the theoretical model of target detection and false alarm probability with a Poisson statistic and the system working at long dead time is established. With typical system design parameters, the target detection probability under different echo intensity and detection number is analyzed, and the influence of four main factors, namely, detection number, echo intensity, noise, and echo position, on the false alarm probability is investigated. The results show that multipulsed detection can improve the target detection probability, and using this developed algorithm, the false alarm probability can be effectively suppressed, to obtain an appropriate false alarm probability; it is suitable that the detection number is selected as 8; and stronger echo intensity, lower noise level, and a more frontal echo position can result in a lower false alarm probability.

A Novel Multiobject Tracking Approach in the Presence of Collision and Division.

This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.