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Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

  • Amir AlansaryEmail author
  • Konstantinos Kamnitsas
  • Alice Davidson
  • Rostislav Khlebnikov
  • Martin Rajchl
  • Christina Malamateniou
  • Mary Rutherford
  • Joseph V. Hajnal
  • Ben Glocker
  • Daniel Rueckert
  • Bernhard Kainz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

Recently, magnetic resonance imaging has revealed to be important for the evaluation of placenta’s health during pregnancy. Quantitative assessment of the placenta requires a segmentation, which proves to be challenging because of the high variability of its position, orientation, shape and appearance. Moreover, image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus, as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field, so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20–38 weeks achieving a Dice score of \(71.95\pm 19.79\,\%\) for healthy fetuses with a fixed scan sequence and \(66.89\pm 15.35\,\%\) for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.

Keywords

Fetal MRI Placenta Segmentation 

Notes

Acknowledgments

We thank NVIDIA for the donation of a Tesla K40 GPU. Medical Interaction Toolkit (http://mitk.org/) was used for some of the figures. This research was supported by the NIHR Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Furthermore, this work was supported by Wellcome Trust and EPSRC IEH award 102431, FP7 ERC 319456, and EPSRC EP/N024494/1. A. Alansary and K. Kamnitsas are supported by the Imperial College PhD Scholarship.

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. Pattern Anal. Mach. Intell. IEEE Trans. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Alansary, A., et al.: Automatic brain localization in fetal mri using superpixel graphs. In: Bhatia, K.K., Lombaert, H. (eds.) MLMMI 2015. LNCS, vol. 9487, pp. 13–22. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-27929-9_2 CrossRefGoogle Scholar
  3. 3.
    Kainz, B., Alansary, A., Malamateniou, C., Keraudren, K., Rutherford, M., Hajnal, J.V., Rueckert, D.: Flexible reconstruction and correction of unpredictable motion from stacks of 2D images. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 555–562. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24571-3_66 CrossRefGoogle Scholar
  4. 4.
    Kamnitsas, K., Ledig, C., Newcombe, V.F., 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 (2016). arXiv preprint arXiv:1603.05959
  5. 5.
    Kanitsar, A., Fleischmann, D., Wegenkittl, R., Felkel, P., Groller, E.: CPR - curved planar reformation. IEEE VIS 2002, 37–44 (2002)Google Scholar
  6. 6.
    Keraudren, K., Kainz, B., Oktay, O., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V., Rueckert, D.: Automated localization of fetal organs in MRI using random forests with steerable features. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 620–627. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24574-4_74 CrossRefGoogle Scholar
  7. 7.
    Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 24, NIPS 2012, pp. 109–117. Curran Associates, Inc. (2011). http://papers.nips.cc/paper/4296-efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials.pdf
  8. 8.
    Routledge, E., Malamateniou, C., Rutherford, M.: Can MR imaging of the placenta demonstrate a distinct placental phenotype in normal and abnormal pregnancies? Technical report, King’s College London, University of London, April 2015Google Scholar
  9. 9.
    Stevenson, G.N., Collins, S.L., Ding, J., Impey, L., Noble, J.A.: 3-D Ultrasound segmentation of the placenta using the random walker algorithm: reliability and agreement. Ultrasound Med. Biol. 41(12), 3182–3193 (2015)CrossRefGoogle Scholar
  10. 10.
    Tagliasacchi, A., Alhashim, I., Olson, M., Zhang, H.: Mean curvature skeletons. Comput. Graph. Forum 31(5), 1735–1744 (2012)CrossRefGoogle Scholar
  11. 11.
    Thame, M., Osmond, C., Bennett, F., Wilks, R., Forrester, T.: Fetal growth is directly related to maternal anthropometry and placental volume. Eur. J. Clin. Nutr. 58(6), 894–900 (2004)CrossRefGoogle Scholar
  12. 12.
    Wang, G., Zuluaga, M.A., Pratt, R., Aertsen, M., David, A.L., Deprest, J., Vercauteren, T., Ourselin, S.: Slic-Seg: slice-by-slice segmentation propagation of the placenta in fetal MRI using one-plane scribbles and online learning. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 29–37. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24574-4_4 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Amir Alansary
    • 1
    Email author
  • Konstantinos Kamnitsas
    • 1
  • Alice Davidson
    • 2
  • Rostislav Khlebnikov
    • 2
  • Martin Rajchl
    • 1
  • Christina Malamateniou
    • 2
  • Mary Rutherford
    • 2
  • Joseph V. Hajnal
    • 2
  • Ben Glocker
    • 1
  • Daniel Rueckert
    • 1
  • Bernhard Kainz
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK
  2. 2.Division of Imaging SciencesKing’s College LondonLondonUK

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