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CapsDeMM: Capsule Network for Detection of Munro’s Microabscess in Skin Biopsy Images

  • Anabik PalEmail author
  • Akshay Chaturvedi
  • Utpal Garain
  • Aditi Chandra
  • Raghunath Chatterjee
  • Swapan Senapati
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

This paper presents an approach for automatic detection of Munro’s Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a machine assisted diagnosis of Psoriasis. The challenge of detecting neutrophils in presence of nucleated cells is solved using the recent advances of deep learning algorithms. Separation of SC layer, extraction of patches from the layer followed by classification of patches with respect to presence or absence of neutrophils form the basis of the overall approach which is effected through an integration of a U-Net based segmentation network and a capsule network for classification. The novel design of the present capsule net leads to a drastic reduction in the number of parameters without any noticeable compromise in the overall performance. The research further addresses the challenge of dealing with Mega-pixel images (in 10X) vis-à-vis Giga-pixel ones (in 40X). The promising result coming out of an experiment on a dataset consisting of 273 real-life images shows that a practical system is possible based on the present research. The implementation of our system is available at https://github.com/Anabik/CapsDeMM.

Keywords

Psoriasis histopathology Biopsy image Neutrophil Munro’s microabscess Stratum corneum Convolutional neural network Capsule network Super-pixel Segmentation Evaluation Dataset 

Notes

Acknowledgments

Authors would like to acknowledge all volunteers who participated in this study.

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Marks, R.M., Knight, A.G., Laidler, P.: Atlas of Skin Pathology, vol. 11. Springer, Netherlands (2012).  https://doi.org/10.1007/978-94-009-4127-4CrossRefGoogle Scholar
  3. 3.
    Pal, A., Chaturvedi, A., Garain, U., Chandra, A., Chatterjee, R.: Severity grading of psoriatic plaques using deep CNN based multi-task learning. In: 23rd International Conference on Pattern Recognition (ICPR 2016), pp. 1478–1483, 4–8 December 2016Google Scholar
  4. 4.
    Pal, A., Garain, U., Chandra, A., Chatterjee, R., Senapati, S.: Psoriasis skin biopsy image segmentation using deep convolutional neural network. Comput. Methods Programs Biomed. 159, 59–69 (2018)CrossRefGoogle Scholar
  5. 5.
    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).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  6. 6.
    Roy, A., Pal, A., Garain, U.: JCLMM: a finite mixture model for clustering of circular-linear data and its application to psoriatic plaque segmentation. Pattern Recognit. 66, 160–173 (2017)CrossRefGoogle Scholar
  7. 7.
    Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3859–3869 (2017)Google Scholar
  8. 8.
    Wang, J., MacKenzie, J.D., Ramachandran, R., Chen, D.Z.: Identifying neutrophils in h&e staining histology tissue images. In: MICCAI Proceedings, Boston, USA, pp. 73–80, 14–18 September 2014.  https://doi.org/10.1007/978-3-319-10404-1_10Google Scholar
  9. 9.
    Wang, J., MacKenzie, J.D., Ramachandran, R., Chen, D.Z.: Neutrophils identification by deep learning and voronoi diagram of clusters. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 226–233. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_27CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anabik Pal
    • 1
    Email author
  • Akshay Chaturvedi
    • 1
  • Utpal Garain
    • 1
  • Aditi Chandra
    • 2
  • Raghunath Chatterjee
    • 2
  • Swapan Senapati
    • 3
  1. 1.CVPR UnitIndian Statistical UnitKolkataIndia
  2. 2.Human Genetics UnitIndian Statistical UnitKolkataIndia
  3. 3.Consultant DermatologistHooghlyIndia

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