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Multi-modal Vertebra Segmentation from MR Dixon for Hybrid Whole-Body PET/MR

  • Christian BuergerEmail author
  • Jochen Peters
  • Irina Waechter-Stehle
  • Frank M. Weber
  • Tobias Klinder
  • Steffen Renisch
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)

Abstract

In this paper, a novel model-based segmentation of the vertebrae is introduced that uses multi-modal image features from Dixon MR images (i.e. water/fat separated). Our primary application is the segmentation of the bony anatomy for the generation of attenuation maps in hybrid PET/MR imaging systems. The focus of this work is on the geometric accuracy of the segmentation from MR. From ground-truth structure delineations on training data sets, image features for a model-based segmentation are trained on both the water and fat images from the Dixon series. For the actual segmentation, both features are used simultaneously to improve both robustness and accuracy compared to single image segmentations. The method is validated on 25 patients by comparing the results to semi-automatically generated ground truth annotations. A mean surface distance error of 1.69 mm over all vertebrae is achieved, leading to an improvement of up to 41 % compared to using a single image alone.

Keywords

Thoracic Vertebra Segmentation Accuracy Mesh Triangle Ground Truth Annotation Generalize Hough Transform 
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.

Notes

Acknowledgments

We thank Bénédicte Delattre and the Hôpitaux Universitaire de Genève, in particular Prof. Osman Ratib, for providing us with the Dixon MR image data.

References

  1. 1.
    Martinez-Moeller, A., Souvatzoglou, M., Delso, G., Bundschuh, R.A., Chefd’hotel, C., Ziegler, S.I., Navab, N., Schwaiger, M., Nekolla, S.G.: Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J. Nuc. Med. 50(4), 520–526 (2009)CrossRefGoogle Scholar
  2. 2.
    Buerger, C., Tsoumpas, C., Aitken, A., King, A.P., Schleyer, P., Schulz, V., Marsden, P.K., Schaeffter, T.: Investigation of MR-Based attenuation correction and motion compensation for hybrid PET/MR. IEEE Trans. Nuc. Sci. 59(5), 1967–1976 (2012)CrossRefGoogle Scholar
  3. 3.
    Berker, Y., Franke, J., Salomon, A., Palmowski, M., Donker, H.C.W., Temur, Y., Mottaghy, F.M., Kuhl, C., Izquierdo-Garcia, D., Fayad, Z.A., Kiessling, F., Schulz, V.: MRI-Based attenuation correction for hybrid PET/MRI systems: a 4-class tissue segmentation technique using a combined ultrashort-echo-time/Dixon MRI sequence. J. Nuc. Med. 53(5), 796–804 (2012)CrossRefGoogle Scholar
  4. 4.
    Michopoulou, S., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., Todd-Pokropek, A.: Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Trans. Biomed. Eng. 56(9), 2225–2231 (2009)CrossRefGoogle Scholar
  5. 5.
    Carballido-Gamio, J., Belongie, S., Majumdar, S.: Normalized cuts in 3-d for spinal MRI segmentation. IEEE Trans. Med. Imaging 23(1), 36–44 (2004)CrossRefGoogle Scholar
  6. 6.
    Michael Kelm, B., Wels, M., Kevin Zhou, S., Seifert, S., Suehling, M., Zheng, Y., Comaniciu, D.: Spine detection in CT and MR using iterated marginal space learning. Med. Image Anal. (2012)Google Scholar
  7. 7.
    Kadoury, S., Labelle, H., Paragios, N.: Spine segmentation in medical images using manifold embeddings and higher-order MRFs (2013)Google Scholar
  8. 8.
    Zhan, Y., Maneesh, D., Harder, M., Zhou, X.S.: Robust MR spine detection using hierarchical learning and local articulated model. In: Medical Image Computing and Computer-Assisted Intervention, pp. 141–148. Springer (2012)Google Scholar
  9. 9.
    Hoad, C.L., Martel, A.L.: Segmentation of MR images for computer-assisted surgery of the lumbar spine. Phys Med Biol 47(19), 3503 (2002)CrossRefGoogle Scholar
  10. 10.
    Ecabert, O., Peters, J., Schramm, H., Lorenz, C., von Berg, J., Walker, M., Vembar, M., Olszewski, M., Subramanyan, K., Lavi, G., Weese, J.: Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27(9), 1189–1201 (2008)CrossRefGoogle Scholar
  11. 11.
    Peters, J., Ecabert, O., Meyer, C., Kneser, R., Weese, J.: Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. Med. Image Anal. 14(1), 70–84 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christian Buerger
    • 1
    Email author
  • Jochen Peters
    • 1
  • Irina Waechter-Stehle
    • 1
  • Frank M. Weber
    • 1
  • Tobias Klinder
    • 1
  • Steffen Renisch
    • 1
  1. 1.Department of Digital ImagingPhilips Technologie GmbH, Innovative Technologies, Research LaboratoriesHamburgGermany

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