Advertisement

Abstract

Precise tracking of intra-operative tissue shift is important for accurate resection of brain tumor. Alignment of pre-interventional magnetic resonance imaging (MRI) to intra-operative ultrasound (iUS) is required to access tissue shift and enable guided surgery. However, accurate and robust image registration needed to relate pre-interventional MRI to iUS images is difficult due to the very different nature of image intensity between modalities. Here we present a framework that can perform non-rigid MRI-ultrasound registration using 3D convolutional neural network (CNN). The framework is composed of three components: feature extractor, deformation field generator and spatial sampler. Our automatic registration framework adopts unsupervised learning approach, allows accurate end-to-end deformable MRI-ultrasound registration. Our proposed method avoids the downfall of intensity-based methods by considering both image intensity and gradient. It achieves competitive registration accuracy on RESECT dataset. In addition, our method takes only about one second to register each image pair, enabling applications such as real time registration.

Keywords

MRI-ultrasound registration 3D CNN Deep learning 

References

  1. 1.
    Avants, B.B., Tustison, N., Song, G.: Advanced Normalization Tools (ANTs). Insight J. 2, 1–35 (2009)Google Scholar
  2. 2.
    Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758–2766 (2015)Google Scholar
  3. 3.
    Fuerst, B., Wein, W., Müller, M., Navab, N.: Automatic ultrasound-MRI registration for neurosurgery using the 2D and 3D LC\(^2\) metric. Med. Image Anal. 18(8), 1312–1319 (2014)CrossRefGoogle Scholar
  4. 4.
    Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)Google Scholar
  5. 5.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  6. 6.
    Miao, S., Wang, Z.J., Zheng, Y., Liao, R.: Real-time 2D/3D registration via CNN regression. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1430–1434. IEEE (2016)Google Scholar
  7. 7.
    de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Cardoso, M.J. (ed.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 204–212. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67558-9_24CrossRefGoogle Scholar
  8. 8.
    Wu, G., Kim, M., Wang, Q., Munsell, B.C., Shen, D.: Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE Trans. Biomed. Eng. 63(7), 1505–1516 (2016)CrossRefGoogle Scholar
  9. 9.
    Xiao, Y., Fortin, M., Unsgård, G., Rivaz, H., Reinertsen, I.: REtroSpective Evaluation of Cerebral Tumors (RESECT): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med. Phys. 44(7), 3875–3882 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Southern University of Science and TechnologyShenzhenChina

Personalised recommendations