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Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

  • Mohsen Ghafoorian
  • Alireza MehrtashEmail author
  • Tina Kapur
  • Nico Karssemeijer
  • Elena Marchiori
  • Mehran Pesteie
  • Charles R. G. Guttmann
  • Frank-Erik de Leeuw
  • Clare M. Tempany
  • Bram van Ginneken
  • Andriy Fedorov
  • Purang Abolmaesumi
  • Bram Platel
  • William M. WellsIII
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.

Notes

Acknowledgements

Research reported in this publication was supported by NIH Grant No. P41EB015898, Natural Sciences and Engineering Research Council (NSERC) of Canada and the Canadian Institutes of Health Research (CIHR) and a VIDI innovational grant from the Netherlands Organisation for Scientific Research (NWO, grant 016.126.351).

References

  1. 1.
    Litjens, G., Kooi, T., Ehteshami Bejnordi, B., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. arXiv preprint arXiv:1702.05747 (2017)
  2. 2.
    Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uden, I., Sanchez, C., Litjens, G., de Leeuw, F., van Ginneken, B., Marchiori, E., Platel, B.: Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities. arXiv preprint arXiv:1610.04834 (2016)
  3. 3.
    Kamnitsas, K., Ledig, C., Newcombe, V., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  4. 4.
    Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V.C.T., Shi, L., Heng, P.A.: Automatic detection of cerebral microbleeds from mr images via 3d convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182–1195 (2016)CrossRefGoogle Scholar
  5. 5.
    Ghafoorian, M., Karssemeijer, N., Heskes, T., Bergkamp, M., Wissink, J., Obels, J., Keizer, K., de Leeuw, F.E., van Ginneken, B., Marchiori, E., Platel, B.: Deep multi-scale location-aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin. NeuroImage Clin. 14, 391–399 (2017)CrossRefGoogle Scholar
  6. 6.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  7. 7.
    Van Opbroek, A., Ikram, M.A., Vernooij, M.W., De Bruijne, M.: Transfer learning improves supervised image segmentation across imaging protocols. IEEE Trans. Med. Imaging 34(5), 1018–1030 (2015)CrossRefGoogle Scholar
  8. 8.
    Cheplygina, V., Pena, I.P., Pedersen, J.H., Lynch, D.A., Sørensen, L., de Bruijne, M.: Transfer learning for multi-center classification of chronic obstructive pulmonary disease. arXiv preprint arXiv:1701.05013 (2017)
  9. 9.
    Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)CrossRefGoogle Scholar
  10. 10.
    Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Todd Hurst, R., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)CrossRefGoogle Scholar
  11. 11.
    Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)CrossRefGoogle Scholar
  12. 12.
    van Norden, A.G., de Laat, K.F., Gons, R.A., van Uden, I.W., van Dijk, E.J., van Oudheusden, L.J., Esselink, R.A., Bloem, B.R., van Engelen, B.G., Zwarts, M.J., Tendolkar, I., Olde-Rikkert, M.G., van der Vlugt, M.J., Zwiers, M.P., Norris, D.G., de Leeuw, F.E.: Causes and consequences of cerebral small vessel disease. The RUN DMC study: a prospective cohort study. Study rationale and protocol. BMC Neurol. 11, 29 (2011)CrossRefGoogle Scholar
  13. 13.
    Ghafoorian, M., Karssemeijer, N., van Uden, I., de Leeuw, F.E., Heskes, T., Marchiori, E., Platel, B.: Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease. Med. Phys. 43(12), 6246–6258 (2016)CrossRefGoogle Scholar
  14. 14.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  15. 15.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  16. 16.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
  17. 17.
    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). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohsen Ghafoorian
    • 1
    • 2
    • 3
  • Alireza Mehrtash
    • 2
    • 4
    Email author
  • Tina Kapur
    • 2
  • Nico Karssemeijer
    • 1
  • Elena Marchiori
    • 3
  • Mehran Pesteie
    • 4
  • Charles R. G. Guttmann
    • 2
  • Frank-Erik de Leeuw
    • 5
  • Clare M. Tempany
    • 2
  • Bram van Ginneken
    • 1
  • Andriy Fedorov
    • 2
  • Purang Abolmaesumi
    • 4
  • Bram Platel
    • 1
  • William M. WellsIII
    • 2
  1. 1.Diagnostic Image Analysis GroupRadboud University Medical CenterNijmegenThe Netherlands
  2. 2.Radiology Department, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  3. 3.Institute for Computing and Information SciencesRadboud UniversityNijmegenThe Netherlands
  4. 4.University of British ColumbiaVancouverCanada
  5. 5.Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands

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