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An accurate cell tracking approach with self-regulated foraging behavior of ant colonies in dynamic microscopy images

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Abstract

The dynamic analysis of cell behavior is fundamental to the evaluation of the correlation between disease and abnormal cell migration. In this study, a self-regulated foraging behavior for cell tracking is proposed under the framework of dual prediction and update of an ant colony and its pheromone field. In the regulated behavior of ant foraging, three strategies are employed: range of foraging, re-sampling, based on the initial distribution of the ant colony; and the stopping criteria of the foraging. The foraging movement of an ant colony is confined to its relevant range determined by the corresponding pheromone field and dynamically varies over iterations. An initial distribution of the ant colonies at each iteration is generated by a Gaussian based re-sampling strategy to regulate an effective search in a centralized manner as a colony. An adaptive stopping criterion of foraging is put forward on the basis of Kullback-Leibler divergence of two approximately Gaussian pheromone fields between two consecutive iterations. The experimental results of the application to cell tracking show that the effectiveness of the algorithm, and demonstrate that it is better than the compared methods.

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References

  1. Zhi X-H, Meng S, Shen H-B (2018) High density cell tracking with accurate centroid detections and active area-based tracklet clustering. Neurocomputing 295:86–97

    Article  Google Scholar 

  2. Arbelle A, Reyes J, Chen JY, Lahav G, Riklin Raviv T (2018) A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos. Med Image Anal 47:140–152

    Article  Google Scholar 

  3. He T, Mao H, Guo J, Yi Z (2017) Cell tracking using deep neural networks with multi-task learning. Image Vis Comput 60:142–153

    Article  Google Scholar 

  4. Padfield D, Rittscher J, Roysam B (2011) Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Med Image Anal 15(4):650–668

    Article  Google Scholar 

  5. Möller M, Burger M, Dieterich P, Schwab A (2014) A framework for automated cell tracking in phase contrast microscopic videos based on normal velocities. J Vis Commun Image Represent 25(2):396–409

    Article  Google Scholar 

  6. Liu M, He Y, Wei Y, Xiang P (2017) Plant cell tracking using Kalman filter based local graph matching. Image Vis Comput 60:154–161

    Article  Google Scholar 

  7. Jiang W, Wu L, Liu S, Liu M (2019) CNN-based two-stage cell segmentation improves plant cell tracking. Pattern Recogn Lett 128:311–317

    Article  Google Scholar 

  8. Mahmood NH (2012) Red blood cells estimation using Hough transform technique. Sig Image Process 3(2):53–64

    Google Scholar 

  9. Zhang T, Jia W, Zhu Y, Yang J (2016) Automatic tracking of neural stem cells in sequential digital images. Biocybern Biomed Eng 36(1):66–75

    Article  Google Scholar 

  10. Nguyen NH, Keller S, Norris E, Huynh TT, Clemens MG, Shin MC (2011) Tracking colliding cells in vivo microscopy. IEEE Trans Biomed Eng 58(8):2391–2400

    Article  Google Scholar 

  11. Yuan L et al (2012) Object tracking with particle filtering in fluorescence microscopy images: application to the motion of neurofilaments in axons. IEEE Trans Med Imaging 31(1):117–130

    Article  Google Scholar 

  12. Vishwanath B. and Seelamantula C. S. (2013) Cell tracking using particle filters and level sets, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013), pp 1–4. https://doi.org/10.1109/TENCON.2013.6718997

  13. Massoudi A, Semenovich D, Sowmya A (2012) Cell tracking and mitosis detection using splitting flow networks in phase-contrast imaging. in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/EMBC.2012.6347193

  14. Magnusson KEG, Jalden J, Gilbert PM, Blau HM (2015) Global linking of cell tracks using the Viterbi algorithm. IEEE Trans Med Imaging 34(4):911–929

    Article  Google Scholar 

  15. Chakraborty A, Roy-Chowdhury AK (2015) Context aware spatio-temporal cell tracking in densely packed multilayer tissues. Med Image Anal 19(1):149–163

    Article  Google Scholar 

  16. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man, Cybern, Part B (Cybernetics) 26(1):29–41

    Article  Google Scholar 

  17. Lu M, Xu B, Nener B (2018) Tracking of multiple cells with ant pheromone field evolution. Eng Appl Artif Intell 72(JUN.):150–161

    Article  Google Scholar 

  18. Xu B, Lu M, Cong J, Nener BD (2020) An ant Colony inspired multi-Bernoulli filter for cell tracking in time-lapse microscopy sequences. IEEE J Biomed Health Inf 24(6):1703–1716

    Article  Google Scholar 

  19. Dorigo M., Caro G. D. and Gambardella L. M.(1999) Ant Algorithms for Discrete Optimization, in Artificial Life 5(2):137–172. https://doi.org/10.1162/106454699568728

  20. Hu X et al (2010) SamACO: Variable Sampling Ant Colony Optimization Algorithm for Continuous Optimization. IEEE transactions on systems, man, and cybernetics, Part B (Cybernetics) 40(6):1555–1566

    Article  Google Scholar 

  21. Zhou Y (2009) Runtime analysis of an ant Colony optimization algorithm for TSP instances. IEEE Trans Evol Comput 13(5):1083–1092

    Article  Google Scholar 

  22. Hsu C, Juang C (2013) Multi-objective continuous-ant-colony-optimized FC for robot wall-following control. IEEE Comput Intell Mag 8(3):28–40

    Article  Google Scholar 

  23. Kuo H, Frederick (2016) Ant colony optimization-based freeform sources for enhancing nanolithographic imaging performance. IEEE Trans Nanotechnol 15(4):599–606

    Article  Google Scholar 

  24. Yin D, du S, Wang S, Guo Z (2015) A direction-guided ant colony optimization method for extraction of urban road information from very-high-resolution images. IEEE J Sel Topics Appl Earth Obs Remote Sens 8(10):4785–4794

    Article  Google Scholar 

  25. Wen X (2020) Modeling and performance evaluation of wind turbine based on ant colony optimization- extre -me learning machine. Appl Soft Comput J 94:106476

    Article  Google Scholar 

  26. Medeiros M., Araújo G., Macedo H., Chella M. and Matos L.(2014) Multi-kernel approach to Parallelization of EM Algorithm for GMM Training, 2014 Brazilian Conference on Intelligent Systems, pp 158–165. https://doi.org/10.1109/BRACIS.2014.38

  27. Lan K, Sekiyama K (2019) Autonomous robot photographer with KL divergence optimization of image composition and human facial direction. Robot Auton Syst 111:132–144

    Article  Google Scholar 

  28. Xu B., Shi J., Lu M., Cong J., Wang L. and Nener B., An Automated Cell Tracking Approach with Multi-Bernoulli Filtering and Ant Colony Labor Division, in IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://doi.org/10.1109/TCBB.2019.2954502

  29. Lu, M., Xu, B., Sheng, A. et al (2014) Modeling analysis of ant system with multiple tasks and its application to spatially adjacent cell state estimate. Appl Intell 41:13–29. https://doi.org/10.1007/s10489-013-0496-7

  30. Xu B, Lu M (2014) An ant-based stochastic searching behavior parameter estimate algorithm for multiple cells tracking. Eng Appl Artif Intell 30:155–167

    Article  Google Scholar 

  31. Bukey CM, Kulkarni SV, Chavan RA (2017) Multi-object tracking using Kalman filter and particle filter, 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI), Chennai, p 1688–1692

  32. Das S, Kale A, Vaswani N (2012) Particle filter with a mode tracker for visual tracking across illumination changes. IEEE Trans Image Process 21(4):2340–2346

    Article  MathSciNet  Google Scholar 

  33. Kim DY, Vo B, Thian A, Choi YS (2017) A generalized labeled multi-Bernoulli tracker for time lapse cell migration, 2017 International conference on control, automation and information sciences (ICCAIS), Chiang Mai, p 20–25

  34. Vo B-N, Vo B-T, Phung D (2014) Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Trans Signal Process 62(24):6554–6567

    Article  MathSciNet  Google Scholar 

  35. Schuhmacher D, Vo B, Vo B (2008) A consistent metric for performance evaluation of multi-object filters. IEEE Trans Signal Process 56(8):3447–3457

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This research is jointly supported by national natural science foundation of China (No.61876024 and No.61673075), and partly by the six talent peaks project in Jiangsu province (No.2017-DZXX-001), 333 Project of Jiangsu Province (No. BRA2019284).

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Authors and Affiliations

  1. School of Electrical & Automatic Engineering, Changshu Institute of Technology, Changshu, 215500, China

    Mingli Lu, Jinliang Cong & Jian Shi

  2. School of Mechanical Engineering, Changshu Institute of Technology, Changshu, 215500, China

    Benlian Xu

  3. Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, 6009, Australia

    Brett Nener

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  1. Mingli Lu
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  2. Benlian Xu
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  3. Brett Nener
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  4. Jinliang Cong
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  5. Jian Shi
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Correspondence to Benlian Xu.

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Lu, M., Xu, B., Nener, B. et al. An accurate cell tracking approach with self-regulated foraging behavior of ant colonies in dynamic microscopy images. Appl Intell 52, 1448–1460 (2022). https://doi.org/10.1007/s10489-021-02424-0

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