Advertisement

Motion Corrected 3D Reconstruction of the Fetal Thorax from Prenatal MRI

  • Bernhard Kainz
  • Christina Malamateniou
  • Maria Murgasova
  • Kevin Keraudren
  • Mary Rutherford
  • Joseph V. Hajnal
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature.

Keywords

Fetal Lung Fetal Magnetic Resonance Image Ground Truth Segmentation Fetal Lung Development Total Variation Denoising 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Anquez, J., Angelini, E., Bloch, I.: Automatic Segmentation of Head Structures on Fetal MRI. In: IEEE ISBI 2009, pp. 109–112. IEEE Press, New York (2009)Google Scholar
  2. 2.
    Criminisi, A., Sharp, T., Blake, A.: GeoS: Geodesic Image Segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 99–112. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer-Verlag New York Inc.(2013)Google Scholar
  4. 4.
    Deshmukh, S., Rubesova, E., Barth, R.: MR Assessment of Normal Fetal Lung Volumes: A Literature Review. AJR Am J. Roentgenol. 194(2), W212–W217 (2010)Google Scholar
  5. 5.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale Vessel Enhancement Filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Ison, M., Donner, R., Dittrich, E., Kasprian, G., Prayer, D., Langs, G.: Fully Automated Brain Extraction and Orientation in Raw Fetal MRI. In: Proc. Workshop on Paediatric and Perinatal Imaging, MICCAI 2012, pp. 17–24 (2012)Google Scholar
  7. 7.
    Jiang, S., Xue, H., Glover, A., Rutherford, M., Rueckert, D., Hajnal, J.V.: MRI of Moving Subjects Using Multislice Snapshot Images with Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies. IEEE T. Med. Imaging 26(7), 967–980 (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kasprian, G., Balassy, C., Brugger, P., Prayer, D.: MRI of Normal and Pathological Fetal Lung Development. Eur. J. Radiol. 57(2), 261–270 (2006)CrossRefGoogle Scholar
  9. 9.
    Keller, T.M., Rake, A., Michel, S.C.A., Seifert, B., Wisser, J., Marincek, B., Kubik-Huch, R.A.: MR Assessment of Fetal Lung Development Using Lung Volumes and Signal Intensities. Eur. Radiol. 14(6), 984–989 (2004)CrossRefGoogle Scholar
  10. 10.
    Keraudren, K., Kyriakopoulou, V., Rutherford, M.A., 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, Part I. LNCS, vol. 8149, pp. 582–589. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Kuklisova-Murgasova, M., Quaghebeur, G., Rutherford, M.A., Hajnal, J.V., Schnabel, J.A.: Reconstruction of Fetal Brain MRI with Intensity Matching and Complete Outlier Removal. Medical Image Analysis 16(8), 1550–1560 (2012)CrossRefGoogle Scholar
  12. 12.
    Langston, C., Kida, K., Reed, M., Thurlbeck, W.M.: Human Lung Growth in Late Gestation and in the Neonate. Am Rev. Respir. Dis. 129(4), 607–613 (1984)Google Scholar
  13. 13.
    Levine, D.: Fetal Magnetic Resonance Imaging. J. Matern Fetal. Neonatal. Med. 15(2), 85–94 (2004)CrossRefGoogle Scholar
  14. 14.
    Moeglin, D., Talmant, C., Duyme, M., Lopez, A.C.: Fetal Lung Volumetry Using Two- and Three-Dimensional Ultrasound. Ultrasound in Obstetrics and Gynecology 25(2), 119–127 (2005)CrossRefGoogle Scholar
  15. 15.
    Rousseau, F., Oubel, E., Pontabry, J., Schweitzer, M., Studholme, C., Koob, M., Dietemann, J.L.: BTK: An Open-Source Toolkit for Fetal Brain MR Image Processing. Computer Methods and Programs in Biomedicine 109(1), 65–73 (2013)CrossRefGoogle Scholar
  16. 16.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear Total Variation Based Noise Removal Algorithms. Physica D: Nonlinear Phenomena 60(14), 259–268 (1992)CrossRefzbMATHGoogle Scholar
  17. 17.
    Skibbe, H., Reisert, M., Schmidt, T., Brox, T., Ronneberger, O., Burkhardt, H.: Fast Rotation Invariant 3D Feature Computation Utilizing Efficient Local Neighborhood Operators. IEEE Trans. Pat. Anal. Mach. Intell. 34(8), 1563–1575 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bernhard Kainz
    • 1
  • Christina Malamateniou
    • 2
  • Maria Murgasova
    • 2
  • Kevin Keraudren
    • 1
  • Mary Rutherford
    • 2
  • Joseph V. Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Imperial College LondonDepartment of ComputingLondonUK
  2. 2.Division of Imaging SciencesKing’s College LondonLondonUK

Personalised recommendations