Advertisement

Hierarchical Multi-Organ Segmentation Without Registration in 3D Abdominal CT Images

  • Vasileios ZografosEmail author
  • Alexander Valentinitsch
  • Markus Rempfler
  • Federico Tombari
  • Bjoern Menze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9601)

Abstract

We present a novel framework for the segmentation of multiple organs in 3D abdominal CT images, which does not require registration with an atlas. Instead we use discriminative classifiers that have been trained on an array of 3D volumetric features and implicitly model the appearance of the organs of interest. We fully leverage all the available data and extract the features from inside supervoxels at multiple levels of detail. Parallel to this, we employ a hierarchical auto-context classification scheme, where the trained classifier at each level is applied back onto the image to provide additional features for the next level. The final segmentation is obtained using a hierarchical conditional random field fusion step. We have tested our approach on 20 contrast enhanced CT images of 8 organs from the VISCERAL dataset and obtained results comparable to the state-of-the-art methods that require very costly registration steps and a much larger corpus of training data. Our method is accurate, fast and general enough that may be applied to a variety of realistic clinical applications and any number of organs.

Keywords

Conditional Random Field Neighbourhood Context Statistical Shape Model Computer Tomography Image Spatial Support 
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.
    Okada, T., Linguraru, M.G., Yoshida, Y., Hori, M., Summers, R.M., Chen, Y.-W., Tomiyama, N., Sato, Y.: Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging. LNCS, vol. 7029, pp. 173–180. Springer, Heidelberg (2012)Google Scholar
  2. 2.
    Chu, C., Oda, M., Kitasaka, T., Misawa, K., Fujiwara, M., Hayashi, Y., Nimura, Y., Rueckert, D., Mori, K.: Multi-organ segmentation based on spatially-divided probabilistic Atlas from 3D abdominal CT images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 165–172. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Oda, M., Nakaoka, T., Kitasaka, T., Furukawa, K., Misawa, K., Fujiwara, M., Mori, K.: Organ segmentation from 3D abdominal CT images based on atlas selection and graph cut. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging. LNCS, vol. 7029, pp. 181–188. Springer, Heidelberg (2012)Google Scholar
  4. 4.
    Wang, Z., Bhatia, K., Glocker, B., Marvao, A., Dawes, T., Misawa, K., Mori, K., Rueckert, D.: Geodesic patch-based segmentation. In: Medical Image Computing and Computer-Assisted Intervention (2014)Google Scholar
  5. 5.
    Lombaert, H., Zikic, D., Criminisi, A., Ayache, N.: Laplacian Forests: semantic image segmentation by guided bagging. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 496–504. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Cuingnet, R., Prevost, R., et al.: Automatic detection and segmentation of Kidneys in 3D CT images using random forests. In: Medical Image Computing and Computer-Assisted Intervention (2012)Google Scholar
  7. 7.
    Holzer, M., Donner, R.: Over-segmentation of 3D medical image volumes based on monogenic cues. In: Proceedings of the CVWW, pp. 35–42 (2014)Google Scholar
  8. 8.
    Kovalev, V.A., Kruggel, F., Gertz, H.J., von Cramon, D.Y.: Three-dimensional texture analysis of MRI brain datasets. IEEE Trans. Med. Imaging 20(5), 424–433 (2001)CrossRefGoogle Scholar
  9. 9.
    Frome, A., Huber, D., Kolluri, R., Bulow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: European Conference on Computer Vision, vol. 3 (2004)Google Scholar
  10. 10.
    Kläser, A., Marszaek, M., Schmid, C.: A spatio temporal descriptor based on 3D Gradients. In: British Machine Vision Conference (2008)Google Scholar
  11. 11.
    Sznitman, R., Becker, C., Fleuret, F., Fua, P.: Fast object detection with entropy-driven evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)Google Scholar
  12. 12.
    Tu, Z.: Auto-context and its application to high-level vision tasks. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  13. 13.
    Komodakis, N., et al.: Performance vs computational efficiency for optimizing single and dynamic MRFs: setting the state of the art with primal-dual strategies. Comput. Vis. Image Underst. 112(1), 14–29 (2008)CrossRefGoogle Scholar
  14. 14.
    Goksel, O., del Toro, O.A.J., Foncubierta-Rodriguez, A., Müller, H.: Proceedings of the VISCERAL Anatomy3 benchmark workshop. In: IEEE International Symposium on Biomedical Imaging, CEUR Workshop Proceedings (2015)Google Scholar
  15. 15.
    Krenn, M., Hanbury, A., Langs, G.: Prototype of silver corpus merging framework (2014)Google Scholar
  16. 16.
    Wolz, R., Chu, C., Misawa, K., Fujiwara, M.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imaging 32(9), 1723–1730 (2013)CrossRefGoogle Scholar
  17. 17.
    del Toro, O.A.J., Goksel, O., Menze, B., Müller, H., Langs, G., Weber, M., Eggel, I.: VISCERAL VISual Concept Extraction challenge in RAdioLogy: ISBI 2014 challenge organization. In: Goksel, O. (ed.) Proceedings of the VISCERAL Challenge at IEEE International Symposium on Biomedical Imaging, CEUR Workshop (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vasileios Zografos
    • 1
    Email author
  • Alexander Valentinitsch
    • 1
    • 2
  • Markus Rempfler
    • 1
  • Federico Tombari
    • 1
    • 2
  • Bjoern Menze
    • 1
  1. 1.Computer Aided Medical Procedures and Augmented RealityTUMMunichGermany
  2. 2.Department of Diagnostic and Interventional NeuroradiologyTUMMunichGermany

Personalised recommendations