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Improved Nuclear Segmentation on Histopathology Images Using a Combination of Deep Learning and Active Contour Model

  • Lei Zhao
  • Tao WanEmail author
  • Hongxiang Feng
  • Zengchang QinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

Automated nuclear segmentation on histopathological images is a prerequisite for a computer-aided diagnosis system. It becomes a challenging problem due to the nucleus occlusion, shape variation, and image background complexity. We present a computerized method for automatically segmenting nuclei in breast histopathology using an integration of a deep learning framework and an improved hybrid active contour (AC) model. A class of edge patches (nuclear boundary), in addition to the two usual classes - background patches and nuclei patches, are used to train a deep convolutional neural network (CNN) to provide accurate initial nuclear locations for the hybrid AC model. We devise a local-to-global scheme through incorporating the local image attributes in conjunction with region and boundary information to achieve robust nuclear segmentation. The experimental results demonstrated that the combination of CNN and AC model was able to gain improved performance in separating both isolated and overlapping nuclei.

Keywords

Convolutional neural network Active contour model Nuclear segmentation Histopathology 

References

  1. 1.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefGoogle Scholar
  2. 2.
    Cheng, L., Xu, H., Xu, J., Gilmore, H., Mandal, M., Madabhushi, A.: Multi-pass adaptive voting for nuclei detection in histopathological images. Sci. Rep. 6, 33985 (2016)CrossRefGoogle Scholar
  3. 3.
    Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  4. 4.
    Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia, pp. 675–678 (2014)Google Scholar
  5. 5.
    Jing, J., Wan, T., Cao, J., Qin, Z.: An improved hybrid active contour model for nuclear segmentation on breast cancer histopathology. In: IEEE International Symposium on Biomedical Imaging, pp. 1155–1158 (2016)Google Scholar
  6. 6.
    Kothari, S., Phan, J., Stokes, T., Wang, M.: Pathology imaging informatics for quantitative analysis of whole-slide images. J. Am. Med. Inf. Assoc. 20(6), 1099–1108 (2013)CrossRefGoogle Scholar
  7. 7.
    Koyuncu, C., Akhan, E., Ersahin, T., Cetin-Atalay, R., Gunduz-Demir, G.: Iterative H-minima-based marker-controlled wathershed for cell nucleus segmentation. Cytometry 89A, 338–349 (2016)CrossRefGoogle Scholar
  8. 8.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  9. 9.
    Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550–1560 (2017)CrossRefGoogle Scholar
  10. 10.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Vis. Pattern Recogn. 1, 1–14 (2015)Google Scholar
  11. 11.
    Taheri, S., Fevens, T., Bui, T.D.: Robust nuclei segmentation in cyto-histopathological images using statistical level set approach with topology preserving constraint. In: SPIE Medical Imaging, pp. 1–10 (2017)Google Scholar
  12. 12.
    Wan, T., Cao, J., Chen, J., Qin, Z.: Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229, 34–44 (2017)CrossRefGoogle Scholar
  13. 13.
    Xing, F., Xie, Y., Yang, L.: An automatic learning-based framework for robust nucleus segmentation. IEEE Trans. Med. Imaging 35(2), 550–566 (2016)CrossRefGoogle Scholar
  14. 14.
    Xu, J., et al.: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2016)CrossRefGoogle Scholar
  15. 15.
    Xue, J., Titterington, D.: t-tests, F-tests and Otsu’s methods for image thresholding. IEEE Trans. Image Process. 20(8), 2392–2396 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Biomedical Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical EngineeringBeihang UniversityBeijingChina
  2. 2.Department of General Thoracic SurgeryChina Japan Friendship HospitalBeijingChina
  3. 3.Intelligent Computing and Machine Learning Lab, School of ASEEBeihang UniversityBeijingChina

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