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Automatic Segmentation of the Intracranial Volume in Fetal MR Images

  • N. Khalili
  • P. Moeskops
  • N. H. P. Claessens
  • S. Scherpenzeel
  • E. Turk
  • R. de Heus
  • M. J. N. L. Benders
  • M. A. Viergever
  • J. P. W. Pluim
  • I. Išgum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)

Abstract

MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). This is challenging because fetal MR images visualize the whole moving fetus and in addition partially visualize the maternal body. This paper presents an automatic method for segmentation of the ICV in fetal MR images. The method employs a multi-scale convolutional neural network in 2D slices to enable learning spatial information from larger context as well as detailed local information. The method is developed and evaluated with 30 fetal T2-weighted MRI scans (average age \(33.2\pm 1.2\) weeks postmenstrual age). The set contains 10 scans acquired in axial, 10 in coronal and 10 in sagittal imaging planes. A reference standard was defined in all images by manual annotation of the intracranial volume in 10 equidistantly distributed slices. The automatic analysis was performed by training and testing the network using scans acquired in the representative imaging plane as well as combining the training data from all imaging planes. On average, the automatic method achieved Dice coefficients of 0.90 for the axial images, 0.90 for the coronal images and 0.92 for the sagittal images. Combining the training sets resulted in average Dice coefficients of 0.91 for the axial images, 0.95 for the coronal images, and 0.92 for the sagittal images. The results demonstrate that the evaluated method achieved good performance in extracting ICV in fetal MR scans regardless of the imaging plane.

Notes

Acknowledgements

This study was sponsored by the Research Program Specialized Nutrition of the Utrecht Center for Food and Health, through a subsidy from the Dutch Ministry of Economic Affairs, the Utrecht Province and the Municipality of Utrecht.

References

  1. 1.
    Anquez, J., Angelini, E.D., Bloch, I.: Automatic segmentation of head structures on fetal MRI. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI 2009, pp. 109–112 (2009)Google Scholar
  2. 2.
    Gholipour, A., Estroff, J.A., Barnewolt, C.E., Connolly, S.A., Warfield, S.K.: Fetal brain volumetry through MRI volumetric reconstruction and segmentation. Int. J. Comput. Assist. Radiol. Surg. 6(3), 329–339 (2011)CrossRefGoogle Scholar
  3. 3.
    Gholipour, A., Rollins, C.K., Velasco-Annis, C., Ouaalam, A., Akhondi-Asl, A., Afacan, O., Ortinau, C.M., Clancy, S., Limperopoulos, C., Yang, E., et al.: A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Scientific Reports 7 (2017)Google Scholar
  4. 4.
    Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp. 448–456 (2015)Google Scholar
  5. 5.
    Ison, M., Donner, R., Dittrich, E., Kasprian, G., Prayer, D., Langs, G.: Fully automated brain extraction and orientation in raw fetal MRI. In: Workshop on Paediatric and Perinatal Imaging, MICCAI, pp. 17–24 (2012)Google Scholar
  6. 6.
    Kainz, B., Steinberger, M., Wein, W., Kuklisova-Murgasova, M., Malamateniou, C., Keraudren, K., Torsney-Weir, T., Rutherford, M., Aljabar, P., Hajnal, J.V., et al.: Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans. Med. Imaging 34(9), 1901–1913 (2015)CrossRefGoogle Scholar
  7. 7.
    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. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  8. 8.
    Keraudren, K., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V., Rueckert, D.: Localisation of the brain in fetal MRI using bundled SIFT features. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 582–589. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40811-3_73 CrossRefGoogle Scholar
  9. 9.
    Kingma, D., Adam, J.B.: A method for stochastic optimisation (2015)Google Scholar
  10. 10.
    Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J., Išgum, I.: Automatic segmentation of mr brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRefGoogle Scholar
  11. 11.
    Rajchl, M., Lee, M.C., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M.A., Hajnal, J.V., Kainz, B., et al.: Deepcut: Object segmentation from bounding box annotations using convolutional neural networks. IEEE Trans. Med. Imaging 36(2), 674–683 (2017)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging. IEEE Trans. Med. Imaging PP(99), 1 (2017)CrossRefGoogle Scholar
  14. 14.
    Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)CrossRefGoogle Scholar
  15. 15.
    Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Taimouri, V., Gholipour, A., Velasco-Annis, C., Estroff, J.A., Warfield, S.K.: A template-to-slice block matching approach for automatic localization of brain in fetal MRI. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 144–147. IEEE (2015)Google Scholar
  17. 17.
    Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR -2016. LNCS, vol. 10129, pp. 95–102. Springer, Cham (2017). doi: 10.1007/978-3-319-52280-7_9 CrossRefGoogle Scholar
  18. 18.
    Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M.A., Hajnal, J.V., Rueckert, D., Aljabar, P.: Automatic quantification of normal cortical folding patterns from fetal brain MRI. Neuroimage 91, 21–32 (2014)CrossRefGoogle Scholar
  19. 19.
    Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)Google Scholar
  20. 20.
    Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • N. Khalili
    • 1
  • P. Moeskops
    • 2
  • N. H. P. Claessens
    • 3
  • S. Scherpenzeel
    • 3
  • E. Turk
    • 3
  • R. de Heus
    • 4
  • M. J. N. L. Benders
    • 3
    • 5
  • M. A. Viergever
    • 1
    • 5
  • J. P. W. Pluim
    • 1
    • 2
  • I. Išgum
    • 1
    • 5
  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  2. 2.Medical Image Analysis Group, Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Department of Neonatology, Wilhelmina Childrens HospitalUniversity Medical Center UtrechtUtrechtThe Netherlands
  4. 4.Department of ObstetricsUniversity Medical Center UtrechtUtrechtThe Netherlands
  5. 5.Brain Center Rudolf MagnusUniversity Medical Center UtrechtUtrechtThe Netherlands

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