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Which Way Round? A Study on the Performance of Stain-Translation for Segmenting Arbitrarily Dyed Histological Images

  • Michael GadermayrEmail author
  • Vitus Appel
  • Barbara M. Klinkhammer
  • Peter Boor
  • Dorit Merhof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Image-to-image translation based on convolutional neural networks recently gained popularity. Especially approaches relying on generative adversarial networks facilitating unpaired training open new opportunities for image analysis. Making use of an unpaired image-to-image translation approach, we propose a methodology to perform stain-independent segmentation of histological whole slide images requiring annotated training data for one single stain only. In this experimental study, we propose and investigate two different pipelines for performing stain-independent segmentation, which are evaluated with three different stain combinations showing different degrees of difficulty. Whereas one pipeline directly translates the images to be evaluated and uses a segmentation model trained on original data, the other “way round” translates the training data in order to finally segment the original images. The results exhibit good performance especially for the first approach and provide evidence that the direction of translation plays a crucial role considering the final segmentation accuracy.

Keywords

Histology Adversarial network Segmentation Kidney 

Notes

Acknowledgement

This work was supported by the German Research Foundation (DFG) under grant no. ME3737/3-1.

References

  1. 1.
    Barker, J., Hoogi, A., Depeursinge, A., Rubin, D.L.: Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med. Image Anal. 30, 60–71 (2016)CrossRefGoogle Scholar
  2. 2.
    BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 460–468 (2016)CrossRefGoogle Scholar
  3. 3.
    Chopra, S., Balakrishnan, S., Gopalan, R.: DLID: deep learning for domain adaptation by interpolating between domains. In: Proceedings of the International Conference on Machine Learning (ICML) (2013)Google Scholar
  4. 4.
    Gadermayr, M., Dombrowski, A., Klinkhammer, B.M., Boor, P., Merhof, D.: CNN cascades for segmenting whole slide images of the kidney. CoRR, https://arxiv.org/abs/1708.00251 (2017)
  5. 5.
    Gadermayr, M., Strauch, M., Klinkhammer, B.M., Djudjaj, S., Boor, P., Merhof, D.: Domain adaptive classification for compensating variability in histopathological whole slide images. In: Campilho, A., Karray, F. (eds.) ICIAR 2016. LNCS, vol. 9730, pp. 616–622. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-41501-7_69CrossRefGoogle Scholar
  6. 6.
    Gupta, L., Klinkhammer, B.M., Boor, P., Merhof, D., Gadermayr, M.: Stain independent segmentation of whole slide images: a case study in renal histology. In: Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI) (2018)Google Scholar
  7. 7.
    Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the International Conference on Computer Vision (CVPR) (2016)Google Scholar
  8. 8.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  9. 9.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV) (2016)Google Scholar
  10. 10.
    Kamnitsas, K., et al: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI), pp. 597–609 (2017)Google Scholar
  11. 11.
    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
  12. 12.
    Sertel, O., Kong, J., Shimada, H., Catalyurek, U., Saltz, J., Gurcan, M.: Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recognit. 42(6), 1093–1103 (2009)CrossRefGoogle Scholar
  13. 13.
    Veta, M., van Diest, P.J., Pluim, J.P.W.: Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 632–639. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_73CrossRefGoogle Scholar
  14. 14.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the International Conference on Computer Vision (ICCV) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Michael Gadermayr
    • 1
    Email author
  • Vitus Appel
    • 1
  • Barbara M. Klinkhammer
    • 2
  • Peter Boor
    • 2
  • Dorit Merhof
    • 1
  1. 1.Institute of Imaging and Computer VisionRWTH Aachen UniversityAachenGermany
  2. 2.Institute Pathology, University Hospital AachenRWTH Aachen UniversityAachenGermany

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