Detection and Registration of Ribs in MRI Using Geometric and Appearance Models

  • Golnoosh Samei
  • Gábor Székely
  • Christine Tanner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Magnetic resonance guided high intensity focused ultrasound (MRgHIFU) is a new type of minimally invasive therapy for treating malignant liver tissues. Since the ribs on the beam path can compromise an effective therapy, detecting them and tracking their motion on MR images is of great importance. However, due to poor magnetic signal emission of bones, ribs cannot be entirely observed in MR. In the proposed method, we take advantage of the accuracy of CT in imaging the ribs to build a geometric ribcage model and combine it with an appearance model of the neighbouring structures of ribs in MR to reconstruct realistic centerlines in MRIs. We have improved our previous method by using a more sophisticated appearance model, a more flexible ribcage model, and a more effective optimization strategy. We decreased the mean error to 2.5 mm, making the method suitable for clinical application. Finally, we propose a rib registration method which conserves the shape and length of ribs, and imposes realistic constraints on their motions, achieving 2.7 mm mean accuracy.


Principle Component Analysis Appearance Model World Coordinate System Angle Point Natural Coordinate System 
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.


  1. 1.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Dantone, M., Gall, J., Fanelli, G., Van Gool, L.: Real-time facial feature detection using conditional regression forests. In: CVPR, pp. 2578–2585. IEEE (2012)Google Scholar
  3. 3.
    Donner, R., Menze, B., Bischof, H., Langs, G.: Global localization of 3D anatomical structures by pre-filtered Hough forests and discrete optimization. Med. Image. Anal. 17(8), 1304–1314 (2013)CrossRefGoogle Scholar
  4. 4.
    Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323–1341 (2012)CrossRefGoogle Scholar
  5. 5.
    Gao, J., Volovick, A., Pekelny, Y., Huang, Z., Cochran, S., Melzer, A.: Focusing through the rib cage for MR-guided transcostal FUS. In: AIP Conf. Proc., vol. 1481(1), pp. 94–99 (2012)Google Scholar
  6. 6.
    Lee, J., Reeves, A.P.: Segmentation of individual ribs from low-dose chest CT. In: SPIE Med. Imaging, p. 76243J (2010)Google Scholar
  7. 7.
    Li, F., Gong, X., Hu, K., Li, C., Wang, Z.: Effect of ribs in HIFU beam path on formation of coagulative necrosis in goat liver. In: AIP Conf. Proc., vol. 829(1), pp. 477–480 (2006)Google Scholar
  8. 8.
    McClelland, J.R., Hawkes, D., Schaeffter, T., King, A.: Respiratory motion models: A review. Med. Image. Anal. 17(1), 19–42 (2013)CrossRefGoogle Scholar
  9. 9.
    Noorda, Y.H., Bartels, L.W., Viergever, M.A., Pluim, J.P.W.: Rib detection in 3D MRI using dynamic programming based on vesselness and ridgeness. In: Yoshida, H., Warfield, S., Vannier, M.W. (eds.) Abdominal Imaging 2013. LNCS, vol. 8198, pp. 212–220. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Samei, G., Tanner, C., Székely, G.: Rib detection in MR images using shape priors and appearance models. In: ISBI, pp. 798–801. IEEE (2014)Google Scholar
  11. 11.
    Staal, J., van Ginneken, B., Viergever, M.A.: Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data. Med. Image. Anal. 11(1), 35 (2007)CrossRefGoogle Scholar
  12. 12.
    Tanner, C., Boye, D., Samei, G., Szekely, G.: Review on 4D models for organ motion compensation. Crit. Rev. Biomed. Eng. 40(2), 135 (2012)CrossRefGoogle Scholar
  13. 13.
    Von Siebenthal, M., Székely, G., Gamper, U., Boesiger, P., Lomax, A., Cattin, P.: 4D MR imaging of respiratory organ motion and its variability. Phys. Med. Biol. 52, 1547 (2007)CrossRefGoogle Scholar
  14. 14.
    Wu, F., Wang, Z., Chen, W., Zhu, H., Bai, J., Zou, J., Li, K., Jin, C., Xie, F., Su, H.: Extracorporeal high intensity focused ultrasound ablation in the treatment of patients with large hepatocellular carcinoma. Ann. Surg. Oncol. 11(12), 1061–1069 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Golnoosh Samei
    • 1
  • Gábor Székely
    • 1
  • Christine Tanner
    • 1
  1. 1.Computer Vision LaboratoryETH ZurichZurichSwitzerland

Personalised recommendations