Using Convolutional Neural Networks in the Problem of Cell Nuclei Segmentation on Histological Images

  • Vladimir KhryashchevEmail author
  • Anton Lebedev
  • Olga Stepanova
  • Anastasiya Srednyakova
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)


Computer-aided diagnostics of cancer pathologies based on histological image segmentation is a promising area in the field of computer vision and machine learning. To date, the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them. The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net. Using this approach allows to get better results in the tasks of medical image segmentation. The developed algorithm based on neural network AlexNet was used for the creation of the automatic markup of the histological image database. The neural network algorithms were trained and tested on the NVIDIA DGX-1 supercomputer using histological images. The results of the research show that the fast algorithm based on neural network U-Net can be successfully used for the histological image segmentation in real medical practice, which is confirmed by the high level of similarity of the obtained markup with the expert one.


Convolutional neural network Cell nuclei segmentation Histological image segmentation 


  1. 1.
    World Health Organization: Cancer, (2018). Accessed 11 January 2018
  2. 2.
    Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)CrossRefGoogle Scholar
  3. 3.
    Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev. Biomed. Eng. 7, 97–114 (2014)CrossRefGoogle Scholar
  4. 4.
    Chougrada, H., Zouakia, H., Alheyane, O.: Deep convolutional neural networks for breast cancer screening. Comput. Methods Programs Biomed. 157, 19–30 (2018)CrossRefGoogle Scholar
  5. 5.
    Mishra, R., Daescu, O., Leavey, P., Rakheja, D., Sengupta, A.: convolutional neural network for histopathological analysis of osteosarcoma. J. Comput. Biol. 25(3), 313–325 (2018)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sharma, H., Zerbe, N., Klempert, I., Hellwich, O., Hufnagl, P.: Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput. Med. Imaging Graph. 61, 2–13 (2017)CrossRefGoogle Scholar
  7. 7.
    Khryashchev, V., Apalkov I., Zvonarev, P.: Neural network adaptive switching median filter for image denoising. In: Proceedings of the International Conference on Computer as a tool (EUROCON 2005), Belgrade, Serbia and Montenegro, pp. 959–962 (2005)Google Scholar
  8. 8.
    Khryashchev, V., Ganin, A., Stepanova, O., Lebedev A.: Age estimation from face images: challenging problem for audience measurement systems. In: Conference of Open Innovation Association, FRUCT, pp. 31–37 (2014)Google Scholar
  9. 9.
    Khryashchev, V., Shmaglit, L., Shemyakov, A.: The application of machine learning techniques to real time audience analysis system. In: Computer Vision in Control Systems-2, Intelligent Systems Reference Library, vol. 75, pp. 49–69. Springer International Publishing, Switzerland (2015)Google Scholar
  10. 10.
    Taneja, A., Ranjan, P., Ujlayan, A.: Multi-cell nuclei segmentation in cervical cancer images by integrated feature vectors. Multimed. Tools Appl. 77, 9271–9290 (2018)CrossRefGoogle Scholar
  11. 11.
    Song, Y., Cai, W., et al.: Region-based progressive localization of cell nuclei in micro-scopic images with data adaptive modeling. BMC Bioinform. 14(1), 173 (2013)CrossRefGoogle Scholar
  12. 12.
    Chen, C., Wang, W., Ozolek, J.A., Lages, N., Altschuler, S.J., Wu, L.F.: A template matching approach for segmenting microscopy images. In: IEEE International Symposium on Biomedical Imaging, pp. 768–771 (2012)Google Scholar
  13. 13.
    Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016)CrossRefGoogle Scholar
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Bartlett, P. (ed.) Advances in Neural Processing Systems 25 (NIPS) 2012, vol. 1, pp. 1097–1105. NIPS, USA (2012)Google Scholar
  15. 15.
    Fischer, P., Ronneberger, O., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J. (eds.) Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2015. LNCS, vol. 9351, pp. 234–341. Springer, Munich (2015)CrossRefGoogle Scholar
  16. 16.
    Minervini, M., Rusu, C., Tsaftaris, S.A.: Learning computationally efficient approximations of complex image segmentation metrics. In: Ramponi, G., Carini, A. (eds.) 8th International Symposium on Image and Signal Processing and Analysis (ISPA) 2013, vol. 1, pp. 60–65. University of Zagreb, University of Trieste, Trieste (2013)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.P.G. DemidovYaroslavl State UniversityYaroslavlRussia

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