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Exploring Deep Convolutional Neural Networks as Feature Extractors for Cell Detection

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12250))

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Abstract

Among different biological studies, the analysis of leukocyte recruitment is fundamental for the comprehension of immunological diseases. The task of detecting and counting cells in these studies is, however, commonly performed by visual analysis. Although many machine learning techniques have been successfully applied to cell detection, they still rely on domain knowledge, demanding high expertise to create handcrafted features capable of describing the object of interest. In this study, we explored the idea of transfer learning by using pre-trained deep convolutional neural networks (DCNN) as feature extractors for leukocytes detection. We tested several DCNN models trained on the ImageNet dataset in six different videos of mice organs from intravital video microscopy. To evaluate our extracted image features, we used the multiple template matching technique in various scenarios. Our results showed an average increase of 5.5% in the \(\text {F}_{1}\)-score values when compared with the traditional application of template matching using only the original image information. Code is available at: https://github.com/brunoggregorio/DCNN-feature-extraction.

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Notes

  1. 1.

    Department of Physiology and Biophysics, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.

  2. 2.

    Special Laboratory of Applied Toxinology (Center of Toxins Immune-Response and Cell Signaling), Butantan Institute, São Paulo, Brazil.

  3. 3.

    https://github.com/brunoggregorio/DCNN-feature-extraction.

References

  1. Acton, S.T., Wethmar, K., Ley, K.: Automatic tracking of rolling leukocytes in vivo. Microvasc. Res. 63(1), 139–148 (2002)

    Article  Google Scholar 

  2. Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J.: Cell tracking via proposal generation and selection. CoRR abs/1705.03386 (2017). http://arxiv.org/abs/1705.03386

  3. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807. IEEE, Honolulu (2017). https://doi.org/10.1109/CVPR.2017.195

  4. Cui, J., Acton, S.T., Lin, Z.: A Monte Carlo approach to rolling leukocyte tracking in vivo. Med. Image Anal. 10(4), 598–610 (2006)

    Article  Google Scholar 

  5. Dong, G., Ray, N., Acton, S.T.: Intravital leukocyte detection using the gradient inverse coefficient of variation. IEEE Trans. Med. Imaging 24(7), 910–924 (2005)

    Article  Google Scholar 

  6. Dos Santos, A.C., Barsante, M.M., Arantes, R.M.E., Bernard, C., Teixeira, M.M., Carvalho-Tavares, J.: CCL2 and CCL5 mediate leukocyte adhesion in experimental autoimmune encephalomyelitis an intravital microscopy study. J. Neuroimmunol. 162(1–2), 122–129 (2005)

    Article  Google Scholar 

  7. Dos Santos, A.C., et al.: Kinin B2 receptor regulates chemokines CCL2 and CCL5 expression and modulates leukocyte recruitment and pathology in experimental autoimmune encephalomyelitis (EAE) in mice. J. Neuroinflammation 5, 49–58 (2008)

    Article  Google Scholar 

  8. Eden, E., Waisman, D., Rudzsky, M., Bitterman, H., Brod, V., Rivlin, E.: An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy. IEEE Trans. Med. Imaging 12(8), 1011–1024 (2005)

    Article  Google Scholar 

  9. Egmont-Petersen, M., Schreiner, U., Tromp, S.C., Lehmann, T.M., Slaaf, D.W., Arts, T.: Detection of leukocytes in contact with the vessel wall from in vivo microscope recordings using a neural network. IEEE Trans. Biomed. Eng. 47(7), 941–951 (2000)

    Article  Google Scholar 

  10. Elisa de Souza, K., Gregório da Silva, B.C., Carvalho-Tavares, J., Ferrari, R.J.: Automatic detection of leukocytes from intravital video microscopy using the phase congruency technique. In: Proceedigns of XI Workshop de Visão Computacional (WVC), pp. 387–391. BDBComp, São Carlos (2015)

    Google Scholar 

  11. Elisa de Souza, K., Gregório da Silva, B.C., Carvalho-Tavares, J., Ferrari, R.J.: Detection of leukocytes in intravital microscopy video images using the phase congruency technique. Revista de Informática Teórica e Aplicada 23(2), 33–55 (2016)

    Google Scholar 

  12. Gavins, F.N.E.: Intravital microscopy: new insights into cellular interactions. Curr. Opinion Pharmacol. 12(5), 601–607 (2012)

    Article  Google Scholar 

  13. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Boston (1992)

    Google Scholar 

  14. Goobic, A.P., Welser, M.E., Acton, S.T., Ley, K.: Biomedical application of target tracking in clutter. In: Conference on Signals, Systems and Computers, vol. 1, pp. 88–92. IEEE, Pacific Grove (2001)

    Google Scholar 

  15. Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31865-1_25

    Chapter  Google Scholar 

  16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE, Las Vegas (2016). https://doi.org/10.1109/CVPR.2016.90

  17. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  18. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708. IEEE (2017)

    Google Scholar 

  19. Khosravi, M., Schafer, R.W.: Template matching based on a grayscale hit-or-miss transform. IEEE Trans. Image Process. 5(6), 1060–1066 (1996)

    Article  Google Scholar 

  20. Lewis, J.P.: Fast template matching. In: Vision Interface, vol. 95. pp. 120–123. Canadian Image Processing and Pattern Recognition Society, Quebec City, Canada (1995)

    Google Scholar 

  21. Mukherjee, D.P., Ray, N., Acton, S.T.: Level set analysis for leukocyte detection and tracking. IEEE Trans. Image Process. 13(4), 562–572 (2004)

    Article  Google Scholar 

  22. Ray, N.: A concave cost formulation for parametric curve fitting: detection of leukocytes from intravital microscopy images. In: Proceedings of the International Conference on Image Processing, pp. 53–56. IEEE, Hong Kong (2010)

    Google Scholar 

  23. Ray, N., Acton, S.T.: Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours. IEEE Trans. Med. Imaging 23(12), 1466–1478 (2004)

    Article  Google Scholar 

  24. Ray, N., Acton, S.T., Ley, K.: Tracking leukocytes in vivo with shape and size constrained active contours. IEEE Trans. Med. Imaging 21(10), 1222–1235 (2002)

    Article  Google Scholar 

  25. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  26. Sahoo, S., Ray, N., Acton, S.T.: Rolling leukocyte detection based on teardrop shape and the gradient inverse coefficient of variation. In: International Conference on Medical Information Visualisation, pp. 29–33. IEEE Computer Society, London (2006)

    Google Scholar 

  27. dos Santos, J.C., et al.: Stingray venom activates IL-33 producing cardiomyocytes, but not mast cell, to promote acute neutrophil-mediated injury. Sci. Rep. 7(7912), 2045–2322 (2017). https://doi.org/10.1038/s41598-017-08395-y

    Article  Google Scholar 

  28. Gregório da Silva, B.C., Carvalho-Tavares, J., Ferrari, R.J.: Detection of leukocytes in intravital video microscopy based on the analysis of Hessian matrix eigenvalues. In: 28th Conference on Graphics, Patterns and Images, pp. 345–352. IEEE, Salvador (2015). https://doi.org/10.1109/SIBGRAPI.2015.48

  29. Gregório da Silva, B.C., Carvalho-Tavares, J., Ferrari, R.J.: Detecting and tracking leukocytes in intravital video microscopy using a Hessian-based spatiotemporal approach. Multidimens. Syst. Sig. Process. 30(2), 815–839 (2018). https://doi.org/10.1007/s11045-018-0581-5

    Article  MATH  Google Scholar 

  30. Gregório da Silva, B.C., Freire, P.G.L., Mello, R.F., Bernardes, D., Carvalho-Tavares, J., Ferrari, R.J.: Técnica de estabilização de movimento em microscopia intravital utilizando métodos de co-registro de imagens. In: XXIV Congresso Brasileiro de Engenharia Biomédica (CBEB), pp. 193–196. CBEB, Uberlândia, MG, Brazil (2014)

    Google Scholar 

  31. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognitions. In: International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

  32. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  33. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826. IEEE, Las Vegas (2016). https://doi.org/10.1109/CVPR.2016.308

  34. Xu, C., Prince, J.L.: Generalized gradient vector flow external forces for active contours. Sig. Process. 71(2), 131–139 (1998)

    Article  Google Scholar 

  35. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  Google Scholar 

  36. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems 27, vol. 2, pp. 3320–3328. Curran Associates Inc, Montreal (2014)

    Google Scholar 

  37. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8697–8710. IEEE (2018)

    Google Scholar 

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Acknowledgments

We would like to thank our collaborators Prof. Juliana Carvalho-Tavares, Ph.D. and Prof. Mônica Lopes-Ferreira, Ph.D. for conducting the biological experiments.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 and by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (grant numbers 2013/26171-6 and 2018/08826-9).

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Authors and Affiliations

  1. Department of Computing, Federal University of São Carlos, São Carlos, SP, 13565-905, Brazil

    Bruno C. Gregório da Silva & Ricardo J. Ferrari

Authors
  1. Bruno C. Gregório da Silva
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  2. Ricardo J. Ferrari
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Corresponding author

Correspondence to Ricardo J. Ferrari .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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da Silva, B.C.G., Ferrari, R.J. (2020). Exploring Deep Convolutional Neural Networks as Feature Extractors for Cell Detection. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_7

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  • DOI: https://doi.org/10.1007/978-3-030-58802-1_7

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