Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning

  • Matthias PerkoniggEmail author
  • Johannes Hofmanninger
  • Björn Menze
  • Marc-André Weber
  • Georg Langs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)


The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we present a transfer learning approach using convolutional neural networks to detect bone lesions in computed tomography imaging data. We compare different learning approaches, and demonstrate that pretraining a convolutional neural network on natural images improves detection accuracy. Also, we describe a patch extraction strategy which encodes different information into each input channel of the networks. We train and evaluate our approach on a dataset with 660 annotated bone lesions, and show how the resulting marker map high-lights lesions in computed tomography imaging data.



This work was supported by the Austrian Science Fund (FWF) project number I2714-B31.


  1. 1.
    Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchial image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255 (2009)Google Scholar
  2. 2.
    Krenn, M., et al.: Datasets created in VISCERAL. Cloud-Based Benchmarking of Medical Image Analysis, pp. 69–84. Springer, Cham (2017). Scholar
  3. 3.
    LeCun, Y., Bottou, L., Bengio, Y., Haggner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  4. 4.
    Roth, H.R., et al.: Efficient false positive reduction in computer-aided detection using convolutional neural networks and random view aggregation. In: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (eds.) Deep Learning and Convolutional Neural Networks for Medical Image Computing. ACVPR, pp. 35–48. Springer, Cham (2017). Scholar
  5. 5.
    Shin, H., Roth, H., Gao, 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
  6. 6.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)Google Scholar
  7. 7.
    Torrey, L., Shavlik, J.: Transfer learning. In: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, 242 (2009)Google Scholar
  8. 8.
    Xu, L., et al.: W-net for whole-body bone lesion detection on \(^{68}\)Ga-Pentixafor PET/CT imaging of multiple myeloma patients. In: Cardoso, M., et al. (eds.) CMMI/SWITCH/RAMBO -2017. LNCS, vol. 10555, pp. 23–30. Springer, Cham (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Matthias Perkonigg
    • 1
    Email author
  • Johannes Hofmanninger
    • 1
  • Björn Menze
    • 2
  • Marc-André Weber
    • 3
  • Georg Langs
    • 1
  1. 1.Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided TherapyMedical University of ViennaViennaAustria
  2. 2.Institute of Biomedical Engineering, Image-based Biomedical ModellingTechnical University of MunichMunichGermany
  3. 3.Institute of Diagnostic and Interventional RadiologyUniversity Medical Center RostockRostockGermany

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