Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNN

  • Xinpeng Xie
  • Yuexiang Li
  • Menglu Zhang
  • Linlin ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)


Nuclei segmentation plays an import role in histopathology images analysis. Deep learning approaches have shown its strength for histopathology images processing in various studies. In this paper, we proposed a novel deep learning framework for automatic nuclei segmentation. The framework adopts the Mask R-CNN as backbone and employs structure-preserving color normalization (SPCN) and watershed for pre- and post-processing. The proposed framework achieved a Dice score of 90.46% on the validation set, which demonstrates its competing segmentation performance.


Nuclei segmentation SPCN Deep learning Instance segmentation 



The work was supported by Natural Science Foundation of China under grands no. 61672357, 61702339 and U1713214, and the Science and Technology Project of Guangdong Province (Grant No. 2018A050501014).


  1. 1.
    Bengtsson, E., Wahlby, C., Lindblad, J.: Robust cell image segmentation methods. Pattern Recogn. Image Anal. 14(2), 157–167 (2004). C/c of Raspoznavaniye Obrazov i Analiz IzobrazheniiGoogle Scholar
  2. 2.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  3. 3.
    Cui, Y., Zhang, G., Liu, Z., et al.: A deep learning algorithm for one-step contour aware nuclei segmentation of histopathological images. arXiv preprint arXiv:1803.02786 (2018)
  4. 4.
    Khoshdeli, M., Parvin, B.: Deep leaning models delineates multiple nuclear phenotypes in H&E stained histology sections. arXiv preprint arXiv:1802.04427 (2018)
  5. 5.
    Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962–1971 (2016)CrossRefGoogle Scholar
  6. 6.
    MICCAI CPM: Digital pathology: segmentation of nuclei in images.
  7. 7.
    MICCAI 2017 segmentation challenge.
  8. 8.
  9. 9.
    He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision (ICCV) (2017)Google Scholar
  10. 10.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)Google Scholar
  11. 11.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). Scholar
  12. 12.
    Girshick, R.: Fast R-CNN. In: International Conference on Computer Vision (ICCV) (2015)Google Scholar
  13. 13.
    Dundar, M.M., et al.: Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans. Biomed. Eng. 58(7), 1977–1984 (2011)CrossRefGoogle Scholar
  14. 14.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv e-print arXiv:1412.6980 (2014)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xinpeng Xie
    • 1
  • Yuexiang Li
    • 2
  • Menglu Zhang
    • 1
  • Linlin Shen
    • 1
    Email author
  1. 1.Computer Vision InstituteShenzhen UniversityShenzhenChina
  2. 2.Youtu LabTencentShenzhenChina

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