Advertisement

Fetal Skull Segmentation in 3D Ultrasound via Structured Geodesic Random Forest

  • Juan J. Cerrolaza
  • Ozan Oktay
  • Alberto Gomez
  • Jacqueline Matthew
  • Caroline Knight
  • Bernhard Kainz
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)

Abstract

Ultrasound is the primary imaging method for prenatal screening and diagnosis of fetal anomalies. Thanks to its non-invasive and non-ionizing properties, ultrasound allows quick, safe and detailed evaluation of the unborn baby, including the estimation of the gestational age, brain and cranium development. However, the accuracy of traditional 2D fetal biometrics is dependent on operator expertise and subjectivity in 2D plane finding and manual marking. 3D ultrasound has the potential to reduce the operator dependence. In this paper, we propose a new random forest-based segmentation framework for fetal 3D ultrasound volumes, able to efficiently integrate semantic and structural information in the classification process. We introduce a new semantic features space able to encode spatial context via generalized geodesic distance transform. Unlike alternative auto-context approaches, this new set of features is efficiently integrated into the same forest using contextual trees. Finally, we use a new structured labels space as alternative to the traditional atomic class labels, able to capture morphological variability of the target organ. Here, we show the potential of this new general framework segmenting the skull in 3D fetal ultrasound volumes, significantly outperforming alternative random forest-based approaches.

Keywords

Random forest Generalized geodesic distance Structured class 

Notes

Acknowledgement

This research was supported in part by the Marie Sklodowska-Curie Actions of the EU Framework Programme for Research and Innovation, under REA grant agreement 706372.

References

  1. 1.
    Dikkeboom, C.M., et al.: The role of three-dimensional ultrasound in visualizing the fetal cranial sutures and fontanels during the second half of pregnancy. Ultrasound Obstet. Gynecol. 24, 412–416 (2004)CrossRefGoogle Scholar
  2. 2.
    Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: A survey. IEEE Trans Med. Imag. 25(8), 987–1010 (2006)CrossRefGoogle Scholar
  3. 3.
    International Society of Ultrasound in Obstetrics and Gynecology: Sonographic examination of the fetal central nervous system: Guidelines for performing the basic examination and the fetal neurosonogram. Ultrasound Obstet. Gynecol. 29, 109–116(2007)Google Scholar
  4. 4.
    Lu, W., et al.: Automated fetal head detection and measurement in ultrasound images by iterative randomized hough transform. Ultrasound Med. Biol. 31(7), 929–936 (2005)CrossRefGoogle Scholar
  5. 5.
    Shen, Y., et al.: Fetal skull analysis in ultrasound images based on iterative randomized Hough transform. SPIE 7265 (2009)Google Scholar
  6. 6.
    Namburete, A.I.L., Noble, J.A.: Fetal cranial segmentation in 2D ultrasound images using shape properties of pixel clusters (2013). ISBI: 720–723Google Scholar
  7. 7.
    Foi, A., et al.: Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation. Comput. Med. Imaging Graph. 38, 774–784 (2014)CrossRefGoogle Scholar
  8. 8.
    Chen, H.C., et al.: Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure. Ultrasound Med. Biol. 38(5), 811–823 (2012)CrossRefGoogle Scholar
  9. 9.
    Kontschieder, P., et al.: Structured labels in random forests for semantic labelling and object detection. TPAMI 36(10), 2104–2116 (2014)CrossRefGoogle Scholar
  10. 10.
    Dollar, P., Zitnick, C.: Structured forests for fast edge detection. In: Proceedings of the ICCV, pp. 1841–1848 (2013)Google Scholar
  11. 11.
    Kontschieder, P., et al.: GeoF: Geodesic forests for learning coupled predictors. In: Proceedings of the CVPR (2013)Google Scholar
  12. 12.
    Oktay, O., et al.: Stratified decision forests for accurate anatomical landmark localization in cardiac images. TMI 36(1), 332–342 (2017)Google Scholar
  13. 13.
    Toivanen, P.J.: New geodesic distance transforms for gray-scale images. Pattern Recogn. Lett. 17, 437–450 (1996)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Butt, K., Lim, K.: Determination of Gestational Age by Ultrasound. SOGC Clinical Practice Guidelines, 303 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Juan J. Cerrolaza
    • 1
  • Ozan Oktay
    • 1
  • Alberto Gomez
    • 2
  • Jacqueline Matthew
    • 2
  • Caroline Knight
    • 2
  • Bernhard Kainz
    • 1
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK
  2. 2.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK

Personalised recommendations