Subtype Cell Detection with an Accelerated Deep Convolution Neural Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


Robust cell detection in histopathological images is a crucial step in the computer-assisted diagnosis methods. In addition, recent studies show that subtypes play an significant role in better characterization of tumor growth and outcome prediction. In this paper, we propose a novel subtype cell detection method with an accelerated deep convolution neural network. The proposed method not only detects cells but also gives subtype cell classification for the detected cells. Based on the subtype cell detection results, we extract subtype cell related features and use them in survival prediction. We demonstrate that our proposed method has excellent subtype cell detection performance and our proposed subtype cell features can achieve more accurate survival prediction.


Cell Detection Zernike Moment Subtype Cell Deep Neural Network Survival Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33415-3_43 CrossRefGoogle Scholar
  2. 2.
    Beck, A.H., Sangoi, A.R., Leung, S., Marinelli, R.J., Nielsen, T.O., van de Vijver, M.J., West, R.B., van de Rijn, M., Koller, D.: Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3, 108ra113 (2011)CrossRefGoogle Scholar
  3. 3.
    Binder, H., Schumacher, M.: Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinform. (2008)Google Scholar
  4. 4.
    Ishwaran, H., Kogalur, U.B., Blackstone, E.H., Lauer, M.S.: Random survival forests. Ann. Appl. Stat. 2(3), 841–860 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  6. 6.
    Li, H., Zhao, R., Wang, X.: Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification. arXiv preprint arXiv:1412.4526 (2014)
  7. 7.
    Liu, F., Yang, L.: A novel cell detection method using deep convolutional neural network and maximum-weight independent set. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 349–357. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24574-4_42 CrossRefGoogle Scholar
  8. 8.
    Pan, H., Xu, Z., Huang, J.: An effective approach for robust lung cancer cell detection. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds.) Patch-MI 2015. LNCS, vol. 9467, pp. 87–94. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-28194-0_11 CrossRefGoogle Scholar
  9. 9.
    Tabesh, A., Teverovskiy, M., Pang, H.Y., Kumar, V.P., Verbel, D., Kotsianti, A., Saidi, O.: Multifeature prostate cancer diagnosis and gleason grading of histological images. IEEE Trans. Med. Imaging 26(10), 1366–1378 (2007)CrossRefGoogle Scholar
  10. 10.
    Wang, H., Xing, F., Su, H., Stromberg, A., Yang, L.: Novel image markers for non-small cell lung cancer classification and survival prediction. BMC Bioinform. 15, 310 (2014)CrossRefGoogle Scholar
  11. 11.
    Xu, Z., Huang, J.: Efficient lung cancer cell detection with deep convolution neural network. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds.) Patch-MI 2015. LNCS, vol. 9467, pp. 79–86. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-28194-0_10 CrossRefGoogle Scholar
  12. 12.
    Yao, J., Ganti, D., Luo, X., Xiao, G., Xie, Y., Yan, S., Huang, J.: Computer-assisted diagnosis of lung cancer using quantitative topology features. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 288–295. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24888-2_35 CrossRefGoogle Scholar
  13. 13.
    Yuan, Y., Failmezger, H., Rueda, O.M., Ali, H.R., Gräf, S., Chin, S.F., Schwarz, R.F., Curtis, C., Dunning, M.J., Bardwell, H., et al.: Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med. 4(157), 157ra143 (2012)CrossRefGoogle Scholar
  14. 14.
    Zhu, X., Yao, J., Luo, X., Xiao, G., Xie, Y., Gazdar, A., Huang, J.: Lung cancer survival prediction from pathological images and genetic data - an integration study. In: IEEE ISBI, pp. 1173–1176, April 2016Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

Personalised recommendations