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Landmark-Based Evaluation of a Block-Matching Registration Framework on the RESECT Pre- and Intra-operative Brain Image Data Set

  • David DrobnyEmail author
  • Marta Ranzini
  • Sébastien Ourselin
  • Tom Vercauteren
  • Marc Modat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11851)

Abstract

In this paper, we describe the application of an established block-matching based registration method to the CuRIOUS 2019 MICCAI registration challenge. Directional and symmetric approaches with different parameters are evaluated to select the most suitable setting of this fully automatic and general registration method. The results can be used as a baseline, for example when evaluating methods specialised in ultrasound (US) to MRI registration or registration of different interventional US (iUS) data. This work is a continuation of our contribution to the CuRIOUS 2018 challenge. We provide a more extensive analysis of main parameters as well as add pre- to post-resection iUS registration to the previous MRI-iUS registration. The proposed approach achieves an average target registration error of 2.68 mm and 1.92 mm for the MR-iUS and the iUS-iUS task respectively.

Keywords

Block-matching Symmetric registration Resection Brain shift Fully automatic MRI iUS 

Notes

Acknowledgments

This work is supported by the UCL EPSRC Centre for Doctoral Training in Medical Imaging [EP/L016478/1], the Wellcome/EPSRC Centre for Interventional and Surgical Sciences [NS/A000050/1], the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] and EPSRC [NS/A000027/1]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P6000 used for this research. This research was supported by the NIHR BRC based at GSTT and KCL.

References

  1. 1.
    Drobny, D., Vercauteren, T., Ourselin, S., Modat, M.: Registration of MRI and iUS data to compensate brain shift using a symmetric block-matching based approach. In: Stoyanov, D., et al. (eds.) POCUS/BIVPCS/CuRIOUS/CPM -2018. LNCS, vol. 11042, pp. 172–178. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01045-4_21CrossRefGoogle Scholar
  2. 2.
    Ebner, M., et al.: Volumetric reconstruction from printed films: enabling 30 year longitudinal analysis in MR neuroimaging. NeuroImage 165, 238–250 (2018)CrossRefGoogle Scholar
  3. 3.
    Markiewicz, P.J., et al.: NiftyPET: a high-throughput software platform for high quantitative accuracy and precision PET imaging and analysis. Neuroinformatics 16(1), 95–115 (2017)CrossRefGoogle Scholar
  4. 4.
    Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. J. Med. Imaging (Bellingham) 1(2), 024003 (2014)CrossRefGoogle Scholar
  5. 5.
    Niftyreg github page. https://github.com/KCL-BMEIS/niftyreg/wiki. Accessed 29 July 2019
  6. 6.
    Ourselin, S., Roche, A., Subsol, G., Pennec, X., Ayache, N.: Reconstructing a 3D structure from serial histological sections. Image Vis. Comput. 19(1–2), 25–31 (2001)CrossRefGoogle Scholar
  7. 7.
    Xiao, Y., et al.: Evaluation of MRI to ultrasound registration methods for brain shiftcorrection: the CuRIOUS2018 challenge. IEEE Trans. Med. Imaging (2019).  https://doi.org/10.1109/TMI.2019.2935060
  8. 8.
    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
  9. 9.
    Yushkevich, P.A., Avants, B.B., Das, S.R., Pluta, J., Altinay, M., Craige, C.: Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3T MRI data. NeuroImage 50(2), 434–445 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
  2. 2.Medical Physics and Biomedical Engineering DepartmentUniversity College LondonLondonUK
  3. 3.School of Biomedical Engineering & Imaging SciencesKing’s College London, King’s Health PartnersLondonUK

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