Localisation of the Brain in Fetal MRI Using Bundled SIFT Features

  • Kevin Keraudren
  • Vanessa Kyriakopoulou
  • Mary Rutherford
  • Joseph V. Hajnal
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)


Fetal MRI is a rapidly emerging diagnostic imaging tool. Its main focus is currently on brain imaging, but there is a huge potential for whole body studies. We propose a method for accurate and robust localisation of the fetal brain in MRI when the image data is acquired as a stack of 2D slices misaligned due to fetal motion. We first detect possible brain locations in 2D images with a Bag-of-Words model using SIFT features aggregated within Maximally Stable Extremal Regions (called bundled SIFT), followed by a robust fitting of an axis-aligned 3D box to the selected regions. We rely on prior knowledge of the fetal brain development to define size and shape constraints. In a cross-validation experiment, we obtained a median error distance of 5.7mm from the ground truth and no missed detection on a database of 59 fetuses. This 2D approach thus allows a robust detection even in the presence of substantial fetal motion.


Support Vector Machine Fetal Brain Fetal Motion Sift Feature Fetal Magnetic Resonance Image 
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.


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  1. 1.
    Anquez, J., Angelini, E., Bloch, I.: Automatic Segmentation of Head Structures on Fetal MRI. In: ISBI, pp. 109–112. IEEE (2009)Google Scholar
  2. 2.
    Carneiro, G., Georgescu, B., Good, S., Comaniciu, D.: Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree. IEEE Transactions on Medical Imaging 27(9), 1342–1355 (2008)CrossRefGoogle Scholar
  3. 3.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual Categorization With Bags of Keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, p. 22 (2004)Google Scholar
  4. 4.
    Feng, S., Zhou, S., Good, S., Comaniciu, D.: Automatic Fetal Face Detection from Ultrasound Volumes via Learning 3D and 2D Information. In: CVPR, pp. 2488–2495. IEEE (2009)Google Scholar
  5. 5.
    Fischler, M., Bolles, R.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle 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: Workshop on Paediatric and Perinatal Imaging, MICCAI, pp. 17–24. Springer (2012)Google Scholar
  7. 7.
    Jiang, S., Xue, H., Glover, A., Rutherford, M., Rueckert, D., Hajnal, J.: MRI of Moving Subjects using Multislice Snapshot Images with Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies. IEEE Transactions on Medical Imaging 26(7), 967–980 (2007)CrossRefGoogle Scholar
  8. 8.
    Lowe, D.: Object Recognition from Local Scale-invariant Features. In: ICCV, vol. 2, pp. 1150–1157. IEEE (1999)Google Scholar
  9. 9.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. In: BMVC, pp. 384–393 (2002)Google Scholar
  10. 10.
    Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Möller, A.M., Nekolla, S., Navab, N.: Fast Multiple Organ Detection and Localization in Whole-body MR Dixon Sequences. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 239–247. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Rousseau, F., Glenn, O., Iordanova, B., Rodriguez-Carranza, C., Vigneron, D., Barkovich, J., Studholme, C., et al.: Registration-based Approach for Reconstruction of High Resolution In Utero Fetal MR Brain Images. Academic Radiology 13(9), 1072–1081 (2006)CrossRefGoogle Scholar
  12. 12.
    Snijders, R., Nicolaides, K.: Fetal Biometry at 14–40 Weeks’ Gestation. Ultrasound in Obstetrics & Gynecology 4(1), 34–48 (2003)CrossRefGoogle Scholar
  13. 13.
    Toews, M., Wells III, W.M.: Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features. Medical Image Analysis (2012)Google Scholar
  14. 14.
    Wu, Z., Ke, Q., Isard, M., Sun, J.: Bundling Features for Large Scale Partial-duplicate Web Image Search. In: CVPR, pp. 25–32. IEEE (2009)Google Scholar
  15. 15.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes using Marginal Space Learning and Steerable Features. IEEE Transactions on Medical Imaging 27(11), 1668–1681 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kevin Keraudren
    • 1
  • Vanessa Kyriakopoulou
    • 2
  • Mary Rutherford
    • 2
  • Joseph V. Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonUK
  2. 2.Centre for the Developing Brain & Department Biomedical Engineering Division of Imaging SciencesKing’s College LondonUK

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