Skip to main content
Top
Published in: Neural Computing and Applications 8/2019

24-11-2017 | Original Article

Stem cell motion-tracking by using deep neural networks with multi-output

Authors: Yangxu Wang, Hua Mao, Zhang Yi

Published in: Neural Computing and Applications | Issue 8/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The aim of automated stem cell motility analysis is reliable processing and evaluation of cell behaviors such as translocation, mitosis, death, and so on. Cell tracking plays an important role in this research. In practice, tracking stem cells is difficult because they have frequent motion, deformation activities, and small resolution sizes in microscopy images. Previous tracking approaches designed to address this problem have been unable to generalize the rapid morphological deformation of cells in a complex living environment, especially for real-time tracking tasks. Herein, a deep learning framework with convolutional structure and multi-output layers is proposed for overcoming stem cell tracking problems. A convolutional structure is used to learn robust cell features through deep features learned on massive visual data by a transfer learning strategy. With multi-output layers, this framework tracks the cell’s motion and simultaneously detects its mitosis as an assistant task. This improves the generalization ability of the model and facilitates practical applications for stem cell research. The proposed framework, tracking and detection neural networks, also contains a particle filter-based motion model, a specialized cell sampling strategy, and corresponding model update strategy. Its current application to a microscopy image dataset of human stem cells demonstrates increased tracking performance and robustness compared with other frequently used methods. Moreover, mitosis detection performance was verified against manually labeled mitotic events of the tracked cell. Experimental results demonstrate good performance of the proposed framework for addressing problems associated with stem cell tracking.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literature
2.
go back to reference Dimarakis I, Levicar N (2007) Cell culture medium composition and translational adult bone marrow-derived stem cell research. Stem Cells 24(12):2888–2890 Dimarakis I, Levicar N (2007) Cell culture medium composition and translational adult bone marrow-derived stem cell research. Stem Cells 24(12):2888–2890
3.
go back to reference Kircher MF, Gambhir SS, Grimm J (2011) Noninvasive cell-tracking methods. Nat Rev Clin Oncol 8(11):677–688CrossRef Kircher MF, Gambhir SS, Grimm J (2011) Noninvasive cell-tracking methods. Nat Rev Clin Oncol 8(11):677–688CrossRef
4.
go back to reference Sacan A, Ferhatosmanoglu H (2008) Celltrack: an open-source software for cell tracking and motility analysis. Bioinformatics 24(14):1647–1649CrossRef Sacan A, Ferhatosmanoglu H (2008) Celltrack: an open-source software for cell tracking and motility analysis. Bioinformatics 24(14):1647–1649CrossRef
5.
go back to reference Bise R, Yin Z, Kanade T (2011) Reliable cell tracking by global data association. In: Proceedings of 2011 IEEE international symposium on biomedical imaging: from nano to macro, vol 48, pp 1004–1010 Bise R, Yin Z, Kanade T (2011) Reliable cell tracking by global data association. In: Proceedings of 2011 IEEE international symposium on biomedical imaging: from nano to macro, vol 48, pp 1004–1010
6.
go back to reference Meijering E, Dzyubachyk O, Smal I (2012) Methods for cell and particle tracking. Methods Enzymol 504:183–200CrossRef Meijering E, Dzyubachyk O, Smal I (2012) Methods for cell and particle tracking. Methods Enzymol 504:183–200CrossRef
7.
go back to reference Alkofahi O, Radke RJ, Goderie SK, Shen Q, Temple S, Roysam B (2006) Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells. Cell Cycle 5(3):327–335CrossRef Alkofahi O, Radke RJ, Goderie SK, Shen Q, Temple S, Roysam B (2006) Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells. Cell Cycle 5(3):327–335CrossRef
8.
go back to reference 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(1):96–105CrossRef 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(1):96–105CrossRef
9.
go back to reference Padfield D, Rittscher J, Roysam B (2011) Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Med Image Anal 15(4):650–668CrossRef Padfield D, Rittscher J, Roysam B (2011) Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Med Image Anal 15(4):650–668CrossRef
10.
go back to reference Ren Y, Xu B, Zhang J, Zhang W (2015) A generalized data association approach for cell tracking in high-density population, In: Proceedings of IEEE international conference on control, automation and information sciences (ICCAIS), pp 502–507 Ren Y, Xu B, Zhang J, Zhang W (2015) A generalized data association approach for cell tracking in high-density population, In: Proceedings of IEEE international conference on control, automation and information sciences (ICCAIS), pp 502–507
11.
go back to reference Mukherjee DP, Ray N, Acton ST (2004) Level set analysis for leukocyte detection and tracking. IEEE Trans Image Process 13(4):562–572CrossRef Mukherjee DP, Ray N, Acton ST (2004) Level set analysis for leukocyte detection and tracking. IEEE Trans Image Process 13(4):562–572CrossRef
12.
go back to reference Lou X, Hamprecht FA (2011) Structured learning for cell tracking. In: Advances in neural information processing systems, pp 1296–1304 Lou X, Hamprecht FA (2011) Structured learning for cell tracking. In: Advances in neural information processing systems, pp 1296–1304
13.
go back to reference Li K, Chen M, Kanade T (2008) Cell population tracking and lineage construction with spatiotemporal context. Med Image Anal 12(5):546–566CrossRef Li K, Chen M, Kanade T (2008) Cell population tracking and lineage construction with spatiotemporal context. Med Image Anal 12(5):546–566CrossRef
14.
go back to reference Maska M, Ulman V, Svoboda D, Matula P, Matula P, Ederra C, Urbiola A (2014) A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11):1609–1617CrossRef Maska M, Ulman V, Svoboda D, Matula P, Matula P, Ederra C, Urbiola A (2014) A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11):1609–1617CrossRef
15.
go back to reference Jiang RM, Crookes D, Luo N, Davidson MW (2010) Live-cell tracking using sift features in DIC microscopic videos. IEEE Trans Bio-med Eng 57(9):2219CrossRef Jiang RM, Crookes D, Luo N, Davidson MW (2010) Live-cell tracking using sift features in DIC microscopic videos. IEEE Trans Bio-med Eng 57(9):2219CrossRef
16.
go back to reference Guo D, Al VDV (2014) Red blood cell tracking using optical flow methods. IEEE J Biomed Health Inform 18(3):991–998CrossRef Guo D, Al VDV (2014) Red blood cell tracking using optical flow methods. IEEE J Biomed Health Inform 18(3):991–998CrossRef
17.
go back to reference Wu Y, Lim J, Yang MH (2013) Online object tracking: A benchmark. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2411–2418 Wu Y, Lim J, Yang MH (2013) Online object tracking: A benchmark. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2411–2418
18.
go back to reference Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel AVD (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):1–48CrossRef Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel AVD (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):1–48CrossRef
19.
go back to reference Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef
20.
go back to reference Zhang H, Cao X, Ho JKL, Chow TWS (2016) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 99:1 Zhang H, Cao X, Ho JKL, Chow TWS (2016) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 99:1
21.
go back to reference Oyedotun OK, Khashman A (2017) Deep learning in vision-based static hand gesture recognition. Neural Comput Appl 28:3941–3951CrossRef Oyedotun OK, Khashman A (2017) Deep learning in vision-based static hand gesture recognition. Neural Comput Appl 28:3941–3951CrossRef
22.
go back to reference Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, vol 25, no 2 Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, vol 25, no 2
23.
go back to reference Wei J, Li XP, Sessler AM (2011) Mitosis detection for stem cell tracking in phase-contrast microscopy images 48(1):2121–2127 Wei J, Li XP, Sessler AM (2011) Mitosis detection for stem cell tracking in phase-contrast microscopy images 48(1):2121–2127
24.
go back to reference Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition
25.
go back to reference Chang C, Ansari R (2005) Kernel particle filter for visual tracking. IEEE Trans Signal Process Lett 12(3):242–245CrossRef Chang C, Ansari R (2005) Kernel particle filter for visual tracking. IEEE Trans Signal Process Lett 12(3):242–245CrossRef
26.
go back to reference Wang N, Yeung DY (2013) Learning a deep compact image representation for visual tracking. In: Advances in neural information processing systems, pp 809–817 Wang N, Yeung DY (2013) Learning a deep compact image representation for visual tracking. In: Advances in neural information processing systems, pp 809–817
28.
29.
go back to reference Abouelnaga Y, Ali OS, Rady H, Moustafa M (2016) Cifar-10: Knn-based ensemble of classifiers. In: Proceedings of international conference on computational science and computational intelligence Abouelnaga Y, Ali OS, Rady H, Moustafa M (2016) Cifar-10: Knn-based ensemble of classifiers. In: Proceedings of international conference on computational science and computational intelligence
30.
go back to reference Carvalho EF, Engel PM (2014) Convolutional sparse feature descriptor for object recognition in cifar-10. In: Intelligent systems, pp 131–135 Carvalho EF, Engel PM (2014) Convolutional sparse feature descriptor for object recognition in cifar-10. In: Intelligent systems, pp 131–135
31.
go back to reference Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37:1834–1848CrossRef Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37:1834–1848CrossRef
32.
go back to reference Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619CrossRef Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619CrossRef
33.
go back to reference Ahuja N (2012) Robust visual tracking via multi-task sparse learning. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2042–2049 Ahuja N (2012) Robust visual tracking via multi-task sparse learning. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2042–2049
34.
go back to reference Kwon J, Lee KM (2010) Visual tracking decomposition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1269–1276 Kwon J, Lee KM (2010) Visual tracking decomposition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1269–1276
Metadata
Title
Stem cell motion-tracking by using deep neural networks with multi-output
Authors
Yangxu Wang
Hua Mao
Zhang Yi
Publication date
24-11-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3291-2

Other articles of this Issue 8/2019

Neural Computing and Applications 8/2019 Go to the issue

Original Article

Generalized BELBIC

Premium Partner