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A survey on automated cell tracking: challenges and solutions

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Abstract

Cell tracking in microscopy images is fundamental to new biological and medical discoveries today. It facilitates the study of the properties of living cells over time. Due to the temporal nature of the cell tracking task, data association is the most difficult aspect of automated cell tracking. Due to the intricate nature of biological, imaging, and algorithmic factors that influence cell segmentation and tracking results, it is challenging to provide a simple and efficient method to determine the appropriate approach for a specific dataset. For example, mitosis, a crucial biological process, plays a key role in correcting trajectories. This research looked at all the challenges of the cell tracking task and the current solutions that have been proposed so far. In this paper, we carefully identified the sources of the tracking challenges and categorized them in a hierarchical diagram to explain the impact of challenges at different levels on the different cell tracking subtasks. Then, after identifying the solutions provided so far, we classified them into three levels: strategic, tactical, and technical. At the strategy level, tracking before detection and tracking by detection are two main approaches. The tactics can be based on cell distance, similarity, overlap, motions, probability, model evaluation, and deep-learning methods. The techniques identified in our analysis include contour evolution, nearest-neighbor linking, morphological-operator-based tracking, similarity-based label propagation, overlap-based label propagation, motion-prediction-based label propagation, graph-based multiple hypothesis tracking, probability-graph-based global optimization, probability-model-based global optimization, and recently developed deep-learning models. By merging cell tracking methods at different levels in one diagram, this classification will help to understand current solutions and provide new insights for cell tracking algorithms. Overall, in this study, we conducted a comprehensive investigation of the challenges of cell tracking and its corresponding solutions, offering a unique source of information.

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References

  1. Mavska M, Ulman V, Svoboda D, Matula P, Matula P, Ederra C, Urbiola A, Espana T, Venkatesan S, Balak DMW, Karas P, Bolckova T, Streitova M, Carthel CA, Coraluppi SP, Harder N, Rohr K, Magnusson KEG, Jalden J, Blau HM, Dzyubachyk O, Krizek P, Hagen GM, Pastor-Escuredo D, Jimenez-Carretero D, Ledesma-Carbayo MJ, Munoz-Barrutia A, Meijering EHW, Kozubek M, Ortiz-de-Solorzano C (2014) A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11):1609–1617. https://doi.org/10.1093/bioinformatics/btu080

    Article  CAS  Google Scholar 

  2. Ong JY, Torres JZ (2019) Dissecting the mechanisms of cell division. J Biol Chem 294:11382–11390. https://doi.org/10.1074/jbc.AW119.008149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Li X, Miao Y, Pal D, Devreotes P (2020) Excitable networks controlling cell migration during development and disease. Semin Cell Dev Biol 100:133–142. https://doi.org/10.1016/j.semcdb.2019.11.001

    Article  CAS  PubMed  Google Scholar 

  4. Freitas JT, Jozic I, Bedogni B (2021) Wound healing assay for melanoma cell migration. Methods Mol 2265:65–71

    Article  CAS  Google Scholar 

  5. Liu JC, Zacksenhouse M, Eisen A, Nofech-Mozes S, Zacksenhaus E (2017) Identification of cell proliferation, immune response and cell migration as critical pathways in a prognostic signature for her2+:er\(\alpha \)- breast cancer. PLoS ONE 12(6):0179223. https://doi.org/10.1371/journal.pone.0179223

    Article  CAS  Google Scholar 

  6. Anjum S, Gurari D (2020) Ctmc: Cell tracking with mitosis detection dataset challenge. In: 2020 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 4228–4237. https://doi.org/10.1109/CVPRW50498.2020.00499

  7. Amat F, Lemon WC, Mossing DP, McDole K, Wan Y, Branson K, Myers EW, Keller PJ (2014) Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nat Methods 11:951–958

    Article  CAS  PubMed  Google Scholar 

  8. Chen X, Zhou X, Wong STC (2006) Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng 53:762–766

    Article  PubMed  Google Scholar 

  9. Yang X, Li H, Zhou X (2006) Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy. IEEE Trans Circuits Syst I: Regul Pap 53:2405–2414

    Article  Google Scholar 

  10. Li F, Zhou X, Ma J, Wong STC (2010) Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis. IEEE Trans Med Imaging 29:96–105. https://doi.org/10.1109/TCSI.2006.884469

    Article  PubMed  Google Scholar 

  11. Holme B, Bjørnerud B, Pedersen NM, Ballina LR, Wesche J, Haugsten EM (2023) Automated tracking of cell migration in phase contrast images with celltraxx. Sci Rep 13. https://doi.org/10.1038/s41598-023-50227-9

  12. Dzyubachyk O, Cappellen WA, Essers J, Niessen WJ, Meijering EHW (2010) Advanced level-set-based cell tracking in time-lapse fluorescence microscopy. IEEE Trans Med Imaging 29:852–867

    Article  PubMed  Google Scholar 

  13. Svoboda D, Ulman V (2017) Mitogen: A framework for generating 3d synthetic time-lapse sequences of cell populations in fluorescence microscopy. IEEE Trans Med Imaging 36:310–321

    Article  PubMed  Google Scholar 

  14. Kok RNU, Hebert L, Huelsz-Prince G, Goos YJ, Zheng X, Bozek K, Stephens GJ, Tans SJ, Zon JS (2020) Organoidtracker: Efficient cell tracking using machine learning and manual error correction. PLoS ONE 15(10):0240802. https://doi.org/10.1371/journal.pone.0240802

    Article  CAS  Google Scholar 

  15. Magnusson KEG (2016) Segmentation and tracking of cells and particles in time-lapse microscopy. PhD thesis, Royal Institute of Technology, Stockholm, Sweden https://nbn-resolving.org/urn:nbn:se:kth:diva-196911

  16. Maddalena L, Antonelli L, Albu A-I, Hada A, Guarracino MR (2022) Artificial intelligence for cell segmentation, event detection, and tracking for label-free microscopy imaging. Algorithms 15:313

    Article  Google Scholar 

  17. ...Ulman V, Maska M, Magnusson KEG, Ronneberger O, Haubold C, Harder N, Matula P, Matula P, Svoboda D, Radojevic M, Smal I, Rohr K, Jalden J, Blau HM, Dzyubachyk O, Lelieveldt BPF, Xiao P, Li Y, Cho S-Y, Dufour AC, Olivo-Marin J-C, Reyes-Aldasoro CC, Solis-Lemus JA, Bensch R, Brox T, Stegmaier J, Mikut R, Wolf S, Hamprecht FA, Esteves T, Quelhas P, Demirel OB, Malmstrom L, Jug F, Tomançak P, Meijering EHW, Munoz-Barrutia A, Kozubek M, Ortiz-de-Solorzano C (2017) An objective comparison of cell tracking algorithms. Nat Methods 14:1141–1152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. ...Ker DFE, Eom S, Sanami S, Bise R, Pascale C, Yin Z, Huh S, Osuna-Highley E, Junkers S, Helfrich CJ, Liang PY, Pan J, Jeong S, Kang SS, Liu J, Nicholson R, Sandbothe MF, Van PT, Liu A, Chen M, Kanade T, Weiss LE, Campbell PG (2018) Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations. Sci Data 5:180237. https://doi.org/10.1038/sdata.2018.237

    Article  PubMed  PubMed Central  Google Scholar 

  19. Matula P, Maska M, Sorokin DV, Matula P, Ortiz-de-Solorzano C, Kozubek M (2015) Cell tracking accuracy measurement based on comparison of acyclic oriented graphs. PLoS ONE 10(12):0144959. https://doi.org/10.1371/journal.pone.0144959

    Article  CAS  Google Scholar 

  20. Meijering E (2012) Cell segmentation: 50 years down the road [life sciences]. IEEE Signal Process Mag 29(5):140–145. https://doi.org/10.1109/MSP.2012.2204190

    Article  ADS  Google Scholar 

  21. Maska M, Ulman V, Delgado-Rodriguez P, Gomez-de-Mariscal E, Necasova T, Guerrero Pena FA, Ren TI, Meyerowitz EM, Scherr T, Loffler K, Mikut R, Guo T, Wang Y, Allebach JP, Bao R, Al-Shakarji NM, Rahmon G, Toubal IE, Palaniappan K, Lux F, Matula P, Sugawara K, Magnusson KEG, Aho L, Cohen AR, Arbelle A, Ben-Haim T, Raviv TR, Isensee F, Jager PF, Maier-Hein KH, Zhu Y, Ederra C, Urbiola A, Meijering E, Cunha A, Munoz-Barrutia A, Kozubek M, Ortiz-de-Solorzano C (2023) The cell tracking challenge: 10 years of objective benchmarking. Nat Methods 20:1010–1020. https://doi.org/10.1038/s41592-023-01879-y

  22. Lux F, Matula P (2019) Dic image segmentation of dense cell populations by combining deep learning and watershed. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp 236–239. 10.1109/ISBI.2019.8759594

  23. Ren W, Wang X, Tian J, Tang Y, Chan AB (2021) Tracking-by-counting: Using network flows on crowd density maps for tracking multiple targets. IEEE Trans Image Process 30:1439–1452

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  24. Wang Z, Yin L, Wang Z (2019) A new approach for cell detection and tracking. IEEE Access 7:99889–99899. https://doi.org/10.1109/ACCESS.2019.2930539

    Article  Google Scholar 

  25. Scherr T, Löffler K, Böhland M, Mikut R (2020) Cell segmentation and tracking using cnn-based distance predictions and a graph-based matching strategy. PLoS ONE 15(12):0243219. https://doi.org/10.1371/journal.pone.0243219

    Article  CAS  Google Scholar 

  26. Hayashida J, Bise R (2019) Cell tracking with deep learning for cell detection and motion estimation in low-frame-rate. In: International conference on medical image computing and computer-assisted intervention, vol 11764, pp 397–405. https://doi.org/10.1007/978-3-030-32239-7_44

  27. Bao R, Al-Shakarji NM, Bunyak F, Palaniappan K (2021) Dmnet: Dual-stream marker guided deep network for dense cell segmentation and lineage tracking. In: 2021 IEEE/CVF international conference on computer vision workshops (ICCVW), pp 3354–3363. https://doi.org/10.1109/ICCVW54120.2021.00375

  28. Akbas CE, Ulman V, Maska M, Jug F, Kozubek M (2019) Automatic fusion of segmentation and tracking labels. In: ECCV 2018, vol 11134, pp 446–454. https://doi.org/10.1007/978-3-030-11024-6_34

  29. Rahmon, G., Bunyak, F., Seetharaman, G., Palaniappan, K.: Motion u-net: Multi-cue encoder-decoder network for motion segmentation. In: 2020 25th International conference on pattern recognition (ICPR), pp. 8125–8132 (2021). https://doi.org/10.1109/ICPR48806.2021.9413211

  30. Zhu Y, Meijering EHW (2021) Automatic improvement of deep learning-based cell segmentation in time-lapse microscopy by neural architecture search. Bioinformatics 37:4844–4850

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein K (2020) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18:203–211. https://doi.org/10.1038/s41592-020-01008-z

    Article  CAS  PubMed  Google Scholar 

  32. Eschweiler D, Spina TV, Choudhury RC, Meyerowitz E, Cunha A, Stegmaier J (2019) Cnn-based preprocessing to optimize watershed-based cell segmentation in 3d confocal microscopy images. In: 16th IEEE International Symposium on Biomedical Imaging (ISBI), pp 223–227. https://doi.org/10.1109/ISBI.2019.8759242

  33. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, vol 9351, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

  34. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings, vol 11045, pp 3–11. https://doi.org/10.1007/978-3-030-00889-5_1

  35. Françani, AO (2022) Analysis of the performance of U-Net neural networks for the segmentation of living cells

  36. Kumar M, Mondal S (2021) Recent developments on target tracking problems: A review. Ocean Engineering 236:109558. https://doi.org/10.1016/j.oceaneng.2021.109558

    Article  Google Scholar 

  37. Dendorfer P, Osep A, Milan A, Schindler K, Cremers D, Reid ID, Roth S, Leal-Taixé L (2021) Motchallenge: A benchmark for single-camera multiple target tracking. Int J Comput Vis 129:845–881. https://doi.org/10.1007/s11263-020-01393-0

    Article  Google Scholar 

  38. Yao Y, Smal I, Grigoriev I, Akhmanova A, Meijering EHW (2020) Deep-learning method for data association in particle tracking. Bioinformatics 36(16):4935–4941. https://doi.org/10.1093/bioinformatics/btaa597

    Article  CAS  PubMed  Google Scholar 

  39. Funamoto K, Zervantonakis IK, Liu Y, Ochs CJ, Kim C, Kamm RD (2012) A novel microfluidic platform for high-resolution imaging of a three-dimensional cell culture under a controlled hypoxic environment. Lab on a Chip 12(22):4855–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ananthakrishnan R, Ehrlicher AJ (2007) The forces behind cell movement. Int J Biol Sci 3:303–317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ortiz-de-Solórzano C, Muñoz-Barrutia A, Meijering EHW, Kozubek M (2015) Toward a morphodynamic model of the cell: Signal processing for cell modeling. IEEE Signal Process Mag 32:20–29

    Article  ADS  Google Scholar 

  42. Gabillon Y, Lepreux S, Oliveira KM (2013) Towards ergonomic user interface composition: A study about information density criterion. In: Human-Computer Interaction. Human-Centred Design Approaches, Methods, Tools, and Environments, pp 211–220. Springer, Berlin, Heidelberg

  43. Jahn SW, Plass M, Moinfar F (2020) Digital pathology: Advantages, limitations and emerging perspectives. J Clin Med 9(11):3697. https://doi.org/10.3390/jcm9113697

    Article  PubMed  PubMed Central  Google Scholar 

  44. Wikipedia contributors (2021) Soft ergonomics — Wikipedia, The Free Encyclopedia. Online; Accessed 22-Jan-2022. https://en.wikipedia.org/w/index.php?title=Soft_ergonomics &oldid=1029247135

  45. Lu Y, Liu A-A, Su Y-T (2021) Chapter 6 - mitosis detection in biomedical images. In: Chen, M. (ed.) Computer Vision for Microscopy Image Analysis. Computer Vision and Pattern Recognition, pp 131–157. https://doi.org/10.1016/B978-0-12-814972-0.00006-0

  46. Gilad T, Reyes J, Chen J-Y, Lahav G, Riklin-Raviv T (2019) Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy. Bioinformatics 35(15):2644–2653. https://doi.org/10.1093/bioinformatics/bty1034

    Article  CAS  PubMed  Google Scholar 

  47. Su Y-T, Lu Y, Chen M, Liu A-A (2022) Deep reinforcement learning-based progressive sequence saliency discovery network for mitosis detection in time-lapse phase-contrast microscopy images. IEEE/ACM Trans Comput Biol Bioinform 19(2):854–865. https://doi.org/10.1109/TCBB.2020.3019042

    Article  PubMed  Google Scholar 

  48. Chen Y, Huo Y (2020) Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis

  49. Winter MR, Mankowski WC, Wait E, Hoz EC, Aguinaldo A, Cohen AR (2019) Separating touching cells using pixel replicated elliptical shape models. IEEE Trans Med Imaging 38:883–893

    Article  PubMed  Google Scholar 

  50. Guerrero Peña FA, Marrero Fernandez PD, Tarr PT, Ren TI, Meyerowitz EM, Cunha A (2020) J regularization improves imbalanced multiclass segmentation. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp 1–5. https://doi.org/10.1109/ISBI45749.2020.9098550

  51. Scherr T, Löffler K, Neumann O, Mikut R (2021) On improving an already competitive segmentation algorithm for the cell tracking challenge - lessons learned. bioRxiv

  52. Arbelle A, Raviv TR (2019) Microscopy cell segmentation via convolutional lstm networks. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp 1008–1012. https://doi.org/10.1109/ISBI.2019.8759447

  53. Chen M (2021) Chapter 5 - cell tracking in time-lapse microscopy image sequences. In: Chen, M. (ed.) Computer Vision for Microscopy Image Analysis. Computer Vision and Pattern Recognition, pp 101–129. https://doi.org/10.1016/B978-0-12-814972-0.00005-9

  54. Spilger R, Imle A, Lee J-Y, Müller B, Fackler OT, Bartenschlager R, Rohr K (2020) A recurrent neural network for particle tracking in microscopy images using future information, track hypotheses, and multiple detections. IEEE Trans Image Process 29:3681–3694. https://doi.org/10.1109/TIP.2020.2964515

    Article  ADS  Google Scholar 

  55. Sixta T, Cao J, Seebach J, Schnittler H, Flach B (2020) Coupling cell detection and tracking by temporal feedback. Machine Vision and Applications 31(24):1. https://doi.org/10.1007/s00138-020-01072-7

    Article  Google Scholar 

  56. Huang, L., McKay, G.N., Durr, N.: A deep learning bidirectional temporal tracking algorithm for automated blood cell counting from non-invasive capillaroscopy videos. In: MICCAI 2021, vol 12908 (2021). https://doi.org/10.1007/978-3-030-87237-3_40

  57. Gilad T, Bray M-A, Carpenter AE, Raviv TR (2015) Symmetry-based mitosis detection in time-lapse microscopy. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp 164–167. https://doi.org/10.1109/ISBI.2015.7163841

  58. Skylaki S, Hilsenbeck O, Schroeder T (2016) Challenges in long-term imaging and quantification of single-cell dynamics. Nat Biotechnol 34:1137–1144

    Article  CAS  PubMed  Google Scholar 

  59. Wikipedia contributors (2020) Batch effect — Wikipedia, The Free Encyclopedia. Online; Accessed 22-Jan-2022. https://en.wikipedia.org/w/index.php?title=Batch_effect &oldid=991656467

  60. Leek JT, Scharpf RB, Bravo HC, Simcha DM, Langmead B, Johnson W, Geman D, Baggerly KA, Irizarry RA (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11:733–739

    Article  CAS  PubMed  Google Scholar 

  61. Johnson W, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics (Oxford, England) 8:118–27. https://doi.org/10.1093/biostatistics/kxj037

    Article  PubMed  Google Scholar 

  62. Ramesh N, Tasdizen T (2021) Chapter 3 - detection and segmentation in microscopy images. In: Chen, M. (ed.) Computer Vision for Microscopy Image Analysis. Computer Vision and Pattern Recognition, pp 43–71. https://doi.org/10.1016/B978-0-12-814972-0.00003-5

  63. Panteli A, Gupta DK, Bruijn N, Gavves E (2020) Siamese tracking of cell behaviour patterns. In: Proceedings of the Third Conference on Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol 121, pp 570–587

  64. Harder N, Mora-Bermúdez F, Godinez WJ, Wünsche A, Eils R, Ellenberg J, Rohr K (2009) Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time. Genome Res 19(11):2113–24

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wollmann T, Gunkel M, Chung I, Erfle H, Rippe K, Rohr K (2019) Gruu-net: Integrated convolutional and gated recurrent neural network for cell segmentation. Med Image Anal 56:68–79. https://doi.org/10.1016/j.media.2019.04.011

    Article  CAS  PubMed  Google Scholar 

  66. Shi J, Xu B, Zhu P, Lu M (2016) Multi-task firework algorithm for cell tracking and contour estimation. In: 2016 International Conference on Control, Automation and Information Sciences (ICCAIS), pp 27–31. https://doi.org/10.1109/ICCAIS.2016.7822430

  67. Forero MG, Morales KD (2021) Evaluation of filtering techniques for cell tracking in confocal microscopy images. In: Tescher AG, Ebrahimi T (eds) Applications of Digital Image Processing XLIV, vol 11842, p 1184214. https://doi.org/10.1117/12.2594392. International Society for Optics and Photonics

  68. Taghanaki SA, Zheng Y, Zhou SK, Georgescu B, Sharma PS, Xu D, Comaniciu D, Hamarneh G (2019) Combo loss: Handling input and output imbalance in multi-organ segmentation. Comput Medical Imaging Graph 75:24–33. https://doi.org/10.1016/j.compmedimag.2019.04.005

    Article  Google Scholar 

  69. Wikipedia contributors: MeVisLab — Wikipedia, The Free Encyclopedia. Online; Accessed 25-Jan-2022 (2021). https://en.wikipedia.org/w/index.php?title=MeVisLab &oldid=1043369447

  70. Schindelin JE, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden CT, Saalfeld S, Schmid B, Tinevez J-Y, White DJ, Hartenstein V, Eliceiri KW, Tomançak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682. https://doi.org/10.1038/nmeth.2019

    Article  CAS  PubMed  Google Scholar 

  71. Chen M (2021) Chapter 9 - open data and software for microscopy image analysis. In: Chen, M. (ed.) Computer Vision for Microscopy Image Analysis. Computer Vision and Pattern Recognition, pp 203–208. https://doi.org/10.1016/B978-0-12-814972-0.00009-6

  72. Shankar K, Perumal E, Elhoseny M, Taher F, Gupta BB, El-Latif AAA (2021) Synergic deep learning for smart health diagnosis of Covid-19 for connected living and smart cities. ACM Trans Internet Technol 22(3):1. https://doi.org/10.1145/3453168

    Article  Google Scholar 

  73. Winter MR, Mankowski WC, Wait E, Temple S, Cohen AR (2016) Lever: software tools for segmentation, tracking and lineaging of proliferating cells. Bioinformatics 32(22):3530–3531

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Cordelières FP, Petit V, Kumasaka MY, Debeir O, Letort V, Gallagher SJ, Larue L (2013) Automated cell tracking and analysis in phase-contrast videos (itrack4u): Development of java software based on combined mean-shift processes. PLoS ONE 8(11):81266. https://doi.org/10.1371/journal.pone.0081266

    Article  ADS  CAS  Google Scholar 

  75. Aragaki H, Ogoh K, Kondo Y, Aoki K (2022) Lim tracker: a software package for cell tracking and analysis with advanced interactivity. Sci Rep 12:2702

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  76. Nishimura, K., Bise, R.: Spatial-temporal mitosis detection in phase-contrast microscopy via likelihood map estimation by 3dcnn. In: 2020 42nd Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 1811–1815 (2020). https://doi.org/10.1109/EMBC44109.2020.9175676

  77. Ciresan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: Medical image computing and computer-assisted intervention (MICCAI), vol 16, pp 411–418

  78. Sebai M, Wang X, Wang T (2020) Maskmitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images. Med Biol Eng Comput 58(7):1603–1623. https://doi.org/10.1007/s11517-020-02175-z

    Article  PubMed  Google Scholar 

  79. Mao Y, Yin Z (2016) A hierarchical convolutional neural network for mitosis detection in phase-contrast microscopy images, vol 9901, pp 685–692. https://doi.org/10.1007/978-3-319-46723-8_79

  80. Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision–ECCV 2018, vol 11211, pp 833–851. https://doi.org/10.1007/978-3-030-01234-2_49

  81. Mahmood T, Arsalan M, Owais M, Lee MB, Park KR (2020) Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster r-cnn and deep cnns. J Clin Med 9(3):749. https://doi.org/10.3390/jcm9030749

    Article  PubMed  PubMed Central  Google Scholar 

  82. Li Y, Rose F, Pietro F, Morin X, Genovesio A (2016) Detection and tracking of overlapping cell nuclei for large scale mitosis analyses. BMC Bioinform 17:183. https://doi.org/10.1186/s12859-016-1030-9

    Article  Google Scholar 

  83. Harder N, Mora-Bermudez F, Godinez WJ, Ellenberg J, Eils R, Rohr K (2007) Determination of mitotic delays in 3d fluorescence microscopy images of human cells using an error-correcting finite state machine. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp 1044–1047. https://doi.org/10.1109/ISBI.2007.357034

  84. Mao Y, Han L, Yin Z (2019) Cell mitosis event analysis in phase contrast microscopy images using deep learning. Med Image Anal 57:32–43

    Article  PubMed  Google Scholar 

  85. Wu B, Kausar T, Xiao Q, Wang M, Wang W, Fan B, Sun D (2017) Ff-cnn: An efficient deep neural network for mitosis detection in breast cancer histological images. In: Medical Image Understanding and Analysis, pp 249–260. https://doi.org/10.1007/978-3-319-60964-5_22

  86. Gallardo GM, Yang F, Ianzini F, Mackey MA, Sonka M (2004) Mitotic cell recognition with hidden markov models. In: Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, San Diego, California, United States, 14-19 February 2004. SPIE Proceedings, vol 5367. https://doi.org/10.1117/12.535778

  87. Liang L, Zhou X, Li F, Wong ST, Huckins J, King RW (2007) Mitosis cell identification with conditional random fields. In: 2007 IEEE/NIH Life Science Systems and Applications Workshop, pp 9–12. https://doi.org/10.1109/LSSA.2007.4400872

  88. Liu A, Li K, Kanade T (2010) Mitosis sequence detection using hidden conditional random fields. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp 580–583. https://doi.org/10.1109/ISBI.2010.5490279

  89. Huh S, Chen M (2011) Detection of mitosis within a stem cell population of high cell confluence in phase-contrast microscopy images. CVPR 2011:1033–1040. https://doi.org/10.1109/CVPR.2011.5995717

    Article  Google Scholar 

  90. Liu A, Li K, Kanade T (2012) A semi-markov model for mitosis segmentation in time-lapse phase contrast microscopy image sequences of stem cell populations. IEEE Trans Med Imaging 31:359–369

    Article  PubMed  Google Scholar 

  91. Lu Y, Liu A, Chen M, Nie W, Su Y (2020) Sequential saliency guided deep neural network for joint mitosis identification and localization in time-lapse phase contrast microscopy images. IEEE J Biomed Health Inf 24:1367–1378

    Article  Google Scholar 

  92. Su Y, Lu Y, Chen M, Liu A (2017) Spatiotemporal joint mitosis detection using cnn-lstm network in time-lapse phase contrast microscopy images. IEEE Access 5:18033–18041

    Article  Google Scholar 

  93. Zhou Y, Mao H, Yi Z (2017) Cell mitosis detection using deep neural networks. Knowl Based Syst 137:19–28

    Article  Google Scholar 

  94. Nie W-Z, Li W-H, Liu A-A, Hao T, Su Y-T (2016) 3d convolutional networks-based mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 1359–1366. https://doi.org/10.1109/CVPRW.2016.171

  95. Nie W, Yan Y, Hao T, Liu C, Su Y (2018) Mitosis event recognition and detection based on evolution of feature in time domain. Mach Vis Appl 29:1249–1256. https://doi.org/10.1007/s00138-018-0913-3

    Article  Google Scholar 

  96. Su Y, Lu Y, Liu J, Chen M, Liu A (2021) Spatio-temporal mitosis detection in time-lapse phase-contrast microscopy image sequences: A benchmark. IEEE Trans Med Imaging 40:1319–1328

    Article  PubMed  Google Scholar 

  97. Ma M, Shi Y, Li W, Gao Y, Xu J (2018) A novel two-stage deep method for mitosis detection in breast cancer histology images. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp 3892–3897. https://doi.org/10.1109/ICPR.2018.8546192

  98. Jiang J, Khan A, Shailja S, Belteton SA, Goebel M, Szymanski DB, Manjunath BS (2023) Segmentation, tracking, and sub-cellular feature extraction in 3d time-lapse images. Sci Rep 13:1. https://doi.org/10.1038/s41598-023-29149-z

    Article  CAS  Google Scholar 

  99. Xu J, Zhou D, Deng D, Li J, Chen C, Liao X, Chen G, Heng P-A (2022) Deep learning in cell image analysis. Intell Comput 3:1–15. https://doi.org/10.34133/2022/9861263

    Article  CAS  Google Scholar 

  100. Maska M, Munoz-Barrutia A, Ortiz-de-Solórzano C (2012) Fast tracking of fluorescent cells based on the chan-vese model. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp 1316–1319. https://doi.org/10.1109/ISBI.2012.6235805

  101. Mavska M, Danek O, Garasa S, Rouzaut A, Munoz-Barrutia A, Ortiz-de-Solorzano C (2013) Segmentation and shape tracking of whole fluorescent cells based on the chan-vese model. IEEE Trans Med Imaging 32:995–1006

    Article  Google Scholar 

  102. Jo H, Han J, Kim YS, Lee Y, Yang S (2021) A novel method for effective cell segmentation and tracking in phase contrast microscopic images. Sensors 21(10):3516. https://doi.org/10.3390/s21103516

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  103. Yu S, Lu Y, Molloy D (2019) A dynamic-shape-prior guided snake model with application in visually tracking dense cell populations. IEEE Trans Image Process 28(3):1513–1527. https://doi.org/10.1109/TIP.2018.2878331

    Article  ADS  MathSciNet  Google Scholar 

  104. Versari C, Stoma S, Batmanov K, Llamosi A, Mroz F, Kaczmarek A, Deyell M, Lhoussaine C, Hersen P, Batt G (2017) Long-term tracking of budding yeast cells in brightfield microscopy: Cellstar and the evaluation platform. J R Soc Interface 14(127):20160705

    Article  PubMed  PubMed Central  Google Scholar 

  105. Dufour AC, Shinin V, Tajbakhsh S, Guillen-Aghion N, Olivo-Marin J-C, Zimmer C (2005) Segmenting and tracking fluorescent cells in dynamic 3-d microscopy with coupled active surfaces. IEEE Trans Image Process 14:1396–1410

    Article  ADS  PubMed  Google Scholar 

  106. Padfield DR, Rittscher J, Thomas N, Roysam B (2009) Spatio-temporal cell cycle phase analysis using level sets and fast marching methods. Med Image Anal 13(1):143–55

    Article  PubMed  Google Scholar 

  107. Zhao M, Jha A, Liu Q, Millis BA, Mahadevan-Jansen A, Lu L, Landman BA, Tyska MJ (2021) Faster mean-shift: Gpu-accelerated clustering for cosine embedding-based cell segmentation and tracking. Med Image Anal 71:102048. https://doi.org/10.1016/j.media.2021.102048

    Article  PubMed  PubMed Central  Google Scholar 

  108. Debeir O, Ham PV, Kiss R, Decaestecker C (2005) Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes. IEEE Trans Med Imaging 24:697–711

    Article  CAS  PubMed  Google Scholar 

  109. Castilla C, Maška M, Sorokin DV, Meijering E, Ortiz-de-Solórzano C (2019) 3-d quantification of filopodia in motile cancer cells. IEEE Trans Med Imaging 38(3):862–872. https://doi.org/10.1109/TMI.2018.2873842

    Article  PubMed  Google Scholar 

  110. Liang P, Chen J, Zhang Y, Wang H, Zheng H, Gu P, Chen D (2020) Intracker: An integrated detector-tracker framework for cell detection and tracking. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp 332–337. https://doi.org/10.1109/CBMS49503.2020.00069

  111. Payer C, Stern D, Neff T, Bischof H, Urschler M (2018) Instance segmentation and tracking with cosine embeddings and recurrent hourglass networks. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II. Lecture Notes in Computer Science, vol 11071, pp 3–11. https://doi.org/10.1007/978-3-030-00934-2_1

  112. Shailja S, Jiang J, Manjunath BS (2021) Semi supervised segmentation and graph-based tracking of 3d nuclei in time-lapse microscopy. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp 385–389. https://doi.org/10.1109/ISBI48211.2021.9433831

  113. Jun B, Ahmadzadegan A, Ardekani A, Solorio L, Vlachos P (2023) Multi-feature-based robust cell tracking. Ann Biomed Eng 51:604–617. https://doi.org/10.1007/s10439-022-03073-1

    Article  PubMed  Google Scholar 

  114. Türetken E, Wang X, Becker CJ, Haubold C, Fua PV (2017) Network flow integer programming to track elliptical cells in time-lapse sequences. IEEE Trans Med Imaging 36:942–951

    Article  PubMed  Google Scholar 

  115. Bensch R, Ronneberger O (2015) Cell segmentation and tracking in phase contrast images using graph cut with asymmetric boundary costs. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp 1220–1223. https://doi.org/10.1109/ISBI.2015.7164093

  116. Dewan MAA, Ahmad MO, Swamy MNS (2011) Tracking biological cells in time-lapse microscopy: An adaptive technique combining motion and topological features. IEEE Trans Biomed Eng 58:1637–1647

    Article  PubMed  Google Scholar 

  117. Wang Y, Mao H, Yi Z (2019) Stem cell motion-tracking by using deep neural networks with multi-output. Neural Comput Appl 31:3455–3467

    Article  Google Scholar 

  118. Zhou Z, Wang F, Xi W, Chen H, Gao P, He C (2019) Joint multi-frame detection and segmentation for multi-cell tracking. In: Image and Graphics, ICIG 2019, pp 435–446. https://doi.org/10.1007/978-3-030-34110-7_36

  119. Xiao P, Zhong L (2017) Tracking of non-dividing cells by using generalized voronoi diagram. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 2684–2687. https://doi.org/10.1109/EMBC.2017.8037410

  120. Vig D, Hamby A, Wolgemuth C (2016) On the quantification of cellular velocity fields. Biophys J 110(7):1469–1475. https://doi.org/10.1016/j.bpj.2016.02.032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Hu T, Huang L, Liu X, Shen H (2019) Real time visual tracking using spatial-aware temporal aggregation network. ArXiv:1908.00692

  122. Reyes-Aldasoro CC, Akerman S, Tozer GM (2008) Measuring the velocity of fluorescently labelled red blood cells with a keyhole tracking algorithm. J Microsc 229:162–173. https://doi.org/10.1111/j.1365-2818.2007.01877.x

    Article  MathSciNet  CAS  PubMed  Google Scholar 

  123. Jin L, Zhao F, Lin W, Zhou X, Kuang C, Nedzved A, Ablameyko S, Liu X, Xu Y (2020) Development of fan–shaped tracker for single particle tracking. Microsc Res Tech 83(9):1056–1065. https://doi.org/10.1002/jemt.23496

    Article  CAS  PubMed  Google Scholar 

  124. Yi J, Wu P, Huang Q, Qu H, Hoeppner DJ, Metaxas DN (2019) Online neural cell tracking using blob-seed segmentation and optical flow. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 1057–1064. https://doi.org/10.1109/CVPRW.2019.00138

  125. Liu K, Lienkamp SS, Shindo A, Wallingford JB, Walz G, Ronneberger O (2014) Optical flow guided cell segmentation and tracking in developing tissue. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp 298–301. https://doi.org/10.1109/ISBI.2014.6867868

  126. Boukari F, Makrogiannis S (2020) Automated cell tracking using motion prediction-based matching and event handling. IEEE/ACM Trans Comput Biol Bioinform 17(3):959–971. https://doi.org/10.1109/TCBB.2018.2875684

    Article  PubMed  Google Scholar 

  127. Lee S, Kim H, Higuchi H, Ishikawa M (2021) Visualization method for the cell-level vesicle transport using optical flow and a diverging colormap. Sensors 21(2). https://doi.org/10.3390/s21020522

  128. Sugawara K, Cevrim C, Averof M (2022) Tracking cell lineages in 3d by incremental deep learning. eLife 11:69380

    Article  Google Scholar 

  129. Xie Y, Liu M, Zhou S, Wang Y (2021) A deep local patch matching network for cell tracking in microscopy image sequences without registration. IEEE/ACM Trans Comput Biol Bioinform 19:3202–3212

    Google Scholar 

  130. Löffler K, Scherr T, Mikut R (2021) A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction. PLoS ONE 16(9):0249257. https://doi.org/10.1371/journal.pone.0249257

    Article  CAS  Google Scholar 

  131. Arbelle A, Drayman N, Bray M-A, Alon U, Carpenter AE, Riklin-Raviv T (2015) Analysis of high-throughput microscopy videos: Catching up with cell dynamics. In: MICCAI, vol 9351. https://doi.org/10.1007/978-3-319-24574-4_26

  132. 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. https://doi.org/10.1016/j.imavis.2016.11.010

    Article  Google Scholar 

  133. Shi J, Lu M (2019) Multiple cell tracking by generalised labelled multi-bernoulli filter. Int J Comput Appl Technol 61(4):273–277. https://doi.org/10.1504/IJCAT.2019.103296

    Article  Google Scholar 

  134. Beard M, Vo B-T, Vo B-N (2020) A solution for large-scale multi-object tracking. IEEE Trans Signal Process 68:2754–2769. https://doi.org/10.1109/TSP.2020.2986136

    Article  ADS  MathSciNet  Google Scholar 

  135. Nguyen TTD, Shim C, Kim W (2021) Biological cell tracking and lineage inference via random finite sets. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp 339–343.https://doi.org/10.1109/ISBI48211.2021.9433957

  136. Belyaev I, Praetorius J-P, Medyukhina A, Figge MT (2021) Enhanced segmentation of label-free cells for automated migration and interaction tracking. Cytometry Part A 99:1218–1229. https://doi.org/10.1002/cyto.a.24466

    Article  Google Scholar 

  137. Ben-Haim, T., Riklin-Raviv, T.: Graph neural network for cell tracking in microscopy videos. In: European Conference on Computer Vision, ECCV 2022, vol. 13681 (2022). https://doi.org/10.1007/978-3-031-19803-8_36

  138. 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. https://doi.org/10.1109/TMI.2014.2370951

    Article  PubMed  Google Scholar 

  139. Schiegg M, Hanslovsky P, Haubold C, Köthe U, Hufnagel L, Hamprecht FA (2015) Graphical model for joint segmentation and tracking of multiple dividing cells. Bioinformatics 31(6):948–56

    Article  CAS  PubMed  Google Scholar 

  140. Liu M, Liu Y, Qian W, Wang Y (2021) Deepseed local graph matching for densely packed cells tracking. IEEE/ACM Trans Comput Biol Bioinform 18(3):1060–1069. https://doi.org/10.1109/TCBB.2019.2936851

    Article  PubMed  Google Scholar 

  141. Lu M, Xu B-L, Nener BD, Cong J, Shi J (2022) An accurate cell tracking approach with self-regulated foraging behavior of ant colonies in dynamic microscopy images. Appl Intell 52:1448–1460

    Article  Google Scholar 

  142. Xu B, Lu M, Shi J, Cong J, Nener BD (2021) A joint tracking approach via ant colony evolution for quantitative cell cycle analysis. IEEE J Biomed Health Inf 25(6):2338–2349. https://doi.org/10.1109/JBHI.2020.3032592

    Article  Google Scholar 

  143. Wu D, Xu B, Lu M (2021) A heuristic and reliable track-to-track data association approach for multi-cell track reconstruction. Appl Intell 51:8162–8175

    Article  Google Scholar 

  144. Wang J, Su X, Zhao L, Zhang J (2020) Deep reinforcement learning for data association in cell tracking. Front Bioeng Biotechnol 8:298. https://doi.org/10.3389/fbioe.2020.00298

    Article  PubMed  PubMed Central  Google Scholar 

  145. Chenouard N, Bloch I, Olivo-Marin J-C (2013) Multiple hypothesis tracking for cluttered biological image sequences. IEEE Trans Pattern Anal Mach Intell 35(11):2736–3750. https://doi.org/10.1109/TPAMI.2013.97

    Article  PubMed  Google Scholar 

  146. Coraluppi SP, Carthel CA (2012) Modified scoring in multiple-hypothesis tracking. J Adv Inf Fusion 7(2):153–164

    Google Scholar 

  147. Coraluppi S, Carthel C, Dickerson SJ, Chiarulli D, Levitan S (2014) Feature-aided multiple-hypothesis tracking and classification of biological cells. In: 17th International Conference on Information Fusion (FUSION), Salamanca, Spain, pp 1–8. https://ieeexplore.ieee.org/document/6916061

  148. Schacherer D, Ritter C, Rohr K (2021) Multiple hypothesis tracking with integrated cell division detection. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp 165–168. https://doi.org/10.1109/ISBI48211.2021.9434153

  149. Wen C, Miura T, Voleti V, Yamaguchi K, Tsutsumi M, Yamamoto K, Otomo K, Fujie Y, Teramoto T, Ishihara T, Aoki K, Nemoto T, Hillman EM, Kimura KD (2021) 3deecelltracker, a deep learning-based pipeline for segmenting and tracking cells in 3d time lapse images. eLife 10:59187. https://doi.org/10.7554/eLife.59187

    Article  Google Scholar 

  150. Moen E, Borba E, Miller G, Schwartz M, Bannon D, Koe N, Camplisson I, Kyme D, Pavelchek C, Price T, Kudo T, Pao E, Graf W, Valen DV (2019) Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

  151. Zhao M, Liu Q, Jha A, Deng R, Yao T, Mahadevan-Jansen A, Tyska MJ, Millis BA, Huo Y (2021) Voxelembed: 3d instance segmentation and tracking with voxel embedding based deep learning. In: MLMI@MICCAI 2021, vol 12966. https://doi.org/10.1007/978-3-030-87589-3_45

  152. Fujimoto K, Mizugaki T, Rajkumar U, Shigeta H, Seno S, Uchida Y, Ishii M, Bafna V, Matsuda H (2021) A cnn-based cell tracking method for multi-slice intravital imaging data. In: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, vol. 35. New York, NY, USA, p 7. https://doi.org/10.1145/3459930.3469559

  153. Dunn KW, Fu C, Ho DJ, Lee S, Han S, Salama P, Delp EJ (2019) Deepsynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. Scientific Reports 9(1):18295. https://doi.org/10.1038/s41598-019-54244-5

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  154. Chan S, Huang C, Bai C, Ding W, Chen S (2021) Res2-unext: a novel deep learning framework for few-shot cell image segmentation. Multim Tools Appl 81:13275–13288. https://doi.org/10.1007/s11042-021-10536-5

    Article  Google Scholar 

  155. Newby JM, Schaefer A, Lee P, Forest MG, Lai SK (2018) Convolutional neural networks automate detection for tracking of submicron-scale particles in 2d and 3d. Proc Natl Acad Sci 115(36):9026–9031. https://doi.org/10.1073/pnas.1804420115

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  156. Cheng H-J, Hsu C-H, Hung C-L, Lin C-Y (2021) A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies. Biomed J 45:465–471

    Article  PubMed  PubMed Central  Google Scholar 

  157. Lugagne J-B, Lin H, Dunlop MJ (2020) Delta: Automated cell segmentation, tracking, and lineage reconstruction using deep learning. PLoS Comput Biol 16(4). https://doi.org/10.1371/journal.pcbi.1007673

  158. Wu X, Wang W, Yang F, Zhang H, Zuo W (2019) Joint learning of siamese network with top-down modulation and hard example mining for visual tracking. Journal of Electronic Imaging 28:053034. https://doi.org/10.1117/1.JEI.28.5.053034

    Article  ADS  Google Scholar 

  159. Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 8971–8980. https://doi.org/10.1109/CVPR.2018.00935

  160. Kimmel JC, Brack AS, Marshall WF (2021) Deep convolutional and recurrent neural networks for cell motility discrimination and prediction. IEEE/ACM Trans Comput Biol Bioinform 18:562–574

    Article  CAS  PubMed  Google Scholar 

  161. Tasdizen T, Sajjadi M, Javanmardi M, Ramesh N (2018) Improving the robustness of convolutional networks to appearance variability in biomedical images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 549–553. https://doi.org/10.1109/ISBI.2018.8363636

  162. Moen E, Bannon D, Kudo T, Graf W, Covert MW, Valen DV (2019) Deep learning for cellular image analysis. Nat Methods 16:1233–1246. https://doi.org/10.1038/s41592-019-0403-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Moghadam MR, Chen YPP (2021) Tracking neutrophil migration in zebrafish model using multi-channel feature learning. IEEE J Biomed Health Inf 25(4):1197–1205. https://doi.org/10.1109/JBHI.2020.3019271

    Article  Google Scholar 

  164. Wu D, Xu B, Lu M, Shi J, Li Z, Guan F, Yang Z (2021) A cell tracking method with deep learning mitosis detection in microscopy images. In: Advances in Swarm Intelligence. ICSI 2021, vol 12690, pp 282–289. https://doi.org/10.1007/978-3-030-78811-7_27

  165. Li R, Gao Q, Rohr K (2021) Multi-object dynamic memory network for cell tracking in time-lapse microscopy images. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp 1029–1032. https://doi.org/10.1109/ISBI48211.2021.9433828

  166. Chen Y, Song Y, Zhang C, Zhang F, O’Donnell L, Chrzanowski W, Cai W (2021) Celltrack r-cnn: A novel end-to-end deep neural network for cell segmentation and tracking in microscopy images. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp 779–782. https://doi.org/10.1109/ISBI48211.2021.9434057

  167. Marvasti-Zadeh SM, Khaghani J, Cheng L, Ghanei-Yakhdan H, Kasaei S (2021) CHASE: robust visual tracking via cell-level differentiable neural architecture search. In: 32nd British Machine Vision Conference 2021, BMVC 2021, Online, November 22-25, 2021, p 324. https://www.bmvc2021-virtualconference.com/assets/papers/1571.pdf

  168. Xu Y, Šep A, Ban Y, Horaud R, Leal-Taixé L, Alameda-Pineda X (2020) How to train your deep multi-object tracker. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 6786–6795. https://doi.org/10.1109/CVPR42600.2020.00682

  169. Bise R, Yin Z, Kanade T (2011) Reliable cell tracking by global data association. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp 1004–1010. https://doi.org/10.1109/ISBI.2011.5872571

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    Reza Yazdi & Hassan Khotanlou

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Yazdi, R., Khotanlou, H. A survey on automated cell tracking: challenges and solutions. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18697-9

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