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Cells Counting with Convolutional Neural Network

  • Run-xu Tan
  • Jun Zhang
  • Peng Chen
  • Bing Wang
  • Yi Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

In this paper, we focus on the problem of cells objects counting. We propose a novel deep learning framework for small object counting named Unite CNN (U-CNN). The U-CNN is used as a regression model to learn the characteristics of input patches. The result of our model output is the density map. Density map can get the exact count of cells, and we can see the location of cell distribution. The regression network predicts a count of the objects that exit inside this frame. Unite CNN learns a multiscale non-linear regression model which uses a pyramid of image patches extracted at multiple scales to perform the final density prediction. We use three different cell counting benchmarks (MAE, MSE, GAME). Our method is tested on the cell pictures under microscope and shown to outperform the state of the art methods.

Keywords

Deep learning Cell counting Unite CNN 

Notes

Acknowledgement

This work is supported by Anhui Provincial Natural Science Foundation (grant number 1608085MF136).

References

  1. 1.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–166 (2004)CrossRefGoogle Scholar
  2. 2.
    Beucher, S.: Watershed, hierarchical segmentation and waterfall algorithm. In: Serra, J., Soille, P. (eds.) Mathematical Morphology and Its Applications to Image Processing, pp. 69–76. Springer, Dordrecht (1994).  https://doi.org/10.1007/978-94-011-1040-2_10CrossRefGoogle Scholar
  3. 3.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249–256 (2010)Google Scholar
  4. 4.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, vol. 60, pp. 1097–1105. Curran Associates Inc. (2012)Google Scholar
  5. 5.
    Lempitsky, V.S., Zisserman, A.: Learning to count objects in images. In: International Conference on Neural Information Processing Systems, vol. 43, pp. 1324–1332. Curran Associates Inc. (2010)Google Scholar
  6. 6.
    Pham, V.Q., Kozakaya, T., Yamaguchi, O., Okada, R.: COUNT forest: co-voting uncertain number of targets using random forest for crowd density estimation. In: IEEE International Conference on Computer Vision, pp. 3253–3261. IEEE (2015)Google Scholar
  7. 7.
    Lempitsky, V.S., Zisserman, A.: Learning to count objects in images. In: International Conference on Neural Information Processing Systems, vol. 43, pp. 1324–1332. Curran Associates Inc. (2010)Google Scholar
  8. 8.
    Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Computer Vision and Pattern Recognition, pp. 833–841. IEEE (2015)Google Scholar
  9. 9.
    Oñoro-Rubio, D., López-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 615–629. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46478-7_38CrossRefGoogle Scholar
  10. 10.
    Boominathan, L., Kruthiventi, S.S.S., Babu, R.V.: CrowdNet: a deep convolutional network for dense crowd counting, pp. 640–644 (2016)Google Scholar
  11. 11.
    Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 9, pp. 2547–2554. IEEE Computer Society (2013)Google Scholar
  12. 12.
    Guerrero-Gómez-Olmedo, R., Torre-Jiménez, B., López-Sastre, R., Maldonado-Bascón, S., Oñoro-Rubio, D.: Extremely overlapping vehicle counting. In: Paredes, R., Cardoso, J.S., Pardo, X.M. (eds.) IbPRIA 2015. LNCS, vol. 9117, pp. 423–431. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19390-8_48CrossRefGoogle Scholar
  13. 13.
    Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Computer Vision and Pattern Recognition, pp. 589–597. IEEE (2016)Google Scholar
  14. 14.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)Google Scholar
  15. 15.
    Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding, pp. 675–678 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Run-xu Tan
    • 1
  • Jun Zhang
    • 1
  • Peng Chen
    • 2
  • Bing Wang
    • 3
  • Yi Xia
    • 1
  1. 1.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina
  2. 2.Institute of Health SciencesAnhui UniversityHefeiChina
  3. 3.School of Electrical and Information EngineeringAnhui University of TechnologyMa AnshanChina

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