Semi-Automatic Anatomical Tree Matching for Landmark-Based Elastic Registration of Liver Volumes

Abstract

One promising approach to register liver volume acquisitions is based on the branching points of the vessel trees as anatomical landmarks inherently available in the liver. Automated tree matching algorithms were proposed to automatically find pair-wise correspondences between two vessel trees. However, to the best of our knowledge, none of the existing automatic methods are completely error free. After a review of current literature and methodologies on the topic, we propose an efficient interaction method that can be employed to support tree matching algorithms with important pre-selected correspondences or after an automatic matching to manually correct wrongly matched nodes. We used this method in combination with a promising automatic tree matching algorithm also presented in this work. The proposed method was evaluated by 4 participants and a CT dataset that we used to derive multiple artificial datasets.

References

1. T. Lange, S. Wörz, K. Rohr, and P. M. Schlag, Landmark-based 3d Elastic Registration of Pre- and Postoperative Liver CT Data, Bildverarbeitung für die Medizin (BVM), 2009.
2. P. Hassenpflug, Gefäß basierte Registrierung zur Computer gestützten Navigation in der Leberchirurgie, Ph.D. Dissertation, Ruprecht-Karls-Universität, 2004.
3. A. Charnoz, V. Agnus, G. Malandain, L. Soler, and M. Tajine, “Tree Matching Applied to Vascular System,” in Graph-based Representation in Pattern Recognition, Lecture Notes in Computer Science, 3434, pp. 183–192, Springer, 2005. View at: Google Scholar
4. T. Lohe, T. Kröger, S. Zidowitz, H.-O. Peitgen, and X. Jiang, “Hierarchical matching of anatomical trees for medical image registration,” in Proceedings of the First International Conference on Medical Biometrics (ICMB), Lecture Notes in Computer Science, 4901, pp. 224–231, Springer, 2008. View at: Google Scholar
5. J. H. Metzen, T. Kröger, A. Schenk, S. Zidowitz, H.-O. Peitgen, and X. Jiang, “Matching of tree Structures for Registration of Medical Images,” in Graph-Based Representations in Pattern Recognition, Springer, 2007. View at: Google Scholar
6. T. Lange, N. Papenberg, S. Heldmann et al., “3D Ultrasound-CT Registration of the Liver Using Combined Landmark-Intensity Information,” International Journal of Computer Assisted Radiology and Surgery (CARS), vol. 4, pp. 79–88, 2009. View at: Google Scholar
7. T. Heimann and H.-P. Meinzer, “Statistical Shape Models for 3D Medical Image Segmentation: A Review,” Medical Image Analysis, vol. 13, no. 4, pp. 543–563, 2009. View at: Google Scholar
8. D. Furukawa, A. Shimizu, and H. Kobatake, “Automatic Liver Segmentation based on Maximum a Posterior Probability Estimation and Level set Method,” in Proc. MICCAI Workshop 3-D Segmentat. Clinic: A Grand Challenge, pp. 117–124, 2007. View at: Google Scholar
9. S. Pan and B. M. Dawant, “Automatic 3-D Segmentation of the Liver from Abdominal CT Images: A Level-set Approach,” in Proc. SPIE Med. Imag.: Image Process, M. Sonka and K. M. Hanson, Eds., vol. 4322, pp. 128–138, 2001. View at: Google Scholar
10. R. Beichel, C. Bauer, A. Bornik, E. Sorantin, and H. Bischof, “Liver Segmentation in CT Data: A Segmentation Refinement Approach,” in Proc. MICCAI Workshop 3-D Segmentat. Clinic: A Grand Challenge, pp. 235–245, 2007. View at: Google Scholar
11. G. Schmidt, M. A. Athelogou, R. Schönmeyer, R. Korn, and G. Binnig, “Cognition Network Technology for a Fully Automated 3-D Segmentation of Liver,” in Proc. MICCAI Workshop 3-D Segmentat. Clinic: A Grand Challenge, pp. 125–133, 2007. View at: Google Scholar
12. T. Heimann et al., “Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets,” IEEE Trans Med Imaging, vol. 28, no. 8, pp. 1251–1265, 2009. View at: Google Scholar
13. C. Kirbas and F. K. H. Quek, “Vessel Extraction Techniques and Algorithms: A Survey,” in Proceedings of the third IEEE Symposium on Bioinformatics and Bioengineering, pp. 238–245, 2003. View at: Google Scholar
14. D. Selle, B. Preim, A. Schenk, and H.-O. Peitgen, “Analysis of Vasculature for Liver Surgical Planning,” IEEE Transactions on Medical Imaging, vol. 21, no. 11, pp. 1344–1357, 2002. View at: Google Scholar
15. Z. Lin, J. Jin, and H. Talbot, “Unseeded Region Growing for 3D Image Segmentation,” in Selected Papers from the Pan-Sydney Workshop on Visualisation, pp. 31–37, Australian Computer Society, Inc., 2001. View at: Google Scholar
16. D. Selle, W. Spindler, B. Preim, and H.-O. Peitgen, “Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning,” in Mathematics Unlimited - 2001 and Beyond, B. Enquist and W. Schmid, Eds., pp. 103–109, Springer, 2000. View at: Google Scholar
17. P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990. View at: Google Scholar
18. Y. Sato, S. Nakajima, N. Shiraga et al., “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Medical Image Analysis, vol. 2, pp. 143–168, 1998. View at: Google Scholar
19. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale Vessel Enhancement Filtering,” in Medical Image Computing and Computer-Assisted Interventation (MICCAI), p. 1496, Springer, 1998. View at: Google Scholar
20. C. Bauer and H. Bischof, “A Novel Approach for Detection of Tubular Objects and its Application to Medical Image Analysis,” in Proceedings of the 30th DAGM Symposium on Pattern Recognition, Springer, 2008. View at: Google Scholar
21. H. E. Bennink, H. C. van Assen, G. J. Streekstra, R. ter Wee, J. A. E. Spaan, and B. M. ter Haar Romeny, “A Novel 3D Multi-scale Lineness Filter for Vessel Detection,” in Medical Image Computing and Computer-Assisted Interventation (MICCAI), N. Ayache, S. Ourselin, and A. J. Maeder, Eds., vol. 4792, pp. 436–443, Springer, 2007. View at: Google Scholar
22. W.-P. Choi, K.-M. Lam, and W.-C. Siu, “Extraction of the Euclidean Skeleton Based on a Connectivity Criterion,” Pattern Recognition, vol. 36, no. 3, pp. 721–729, 2003. View at: Google Scholar
23. L. Latecki, Q. Li, X. Bai, and W. Liu, “Skeletonization Using SSM of the Distance Transform,” in International Conference on Image Processing (ICIP), vol. 5, pp. 349–352, 2007. View at: Google Scholar
24. M. Ilg and R. Ogniewicz, “The Application of Voronoi Skeletons to Perceptual Grouping in Line Images,” in Proceedings of the 11th International Conference on Pattern Recognition, vol. 3, pp. 382–385, 1992. View at: Google Scholar
25. K. Palágyi, J. Tschirren, and M. Sonka, “Quantitative Analysis of Intrathoracic Airway Trees: Methods and Validation,” in Information Processing in Medical Imaging, vol. 2732, pp. 222–233, Springer, 2003. View at: Google Scholar
26. T. Lee, R. L. Kashyap, and C. Chu, “Building Skeleton Models via 3-D Medical Surface/Axis Thinning Algorithms,” in Graphical Models and Image Processing, vol. 56, pp. 462–478, Academic Press, 1994. View at: Google Scholar
27. M. Pelillo, K. Siddiqi, and S. Zucker, “Matching Hierarchical Structures Using Association Graphs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1105–1120, 1999. View at: Google Scholar
28. S. Gold and A. Rangarajan, “A Graduated Assignment Algorithm for Graph Matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 4, pp. 377–388, 1996. View at: Google Scholar
29. S. Medasani, R. Krishnapuram, and Y. Choi, “Graph Matching by Relaxation of Fuzzy Assignments,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 1, pp. 173–182, 2001. View at: Google Scholar
30. A. Charnoz, V. Agnus, G. Malandain, L. Soler, and M. Tajine, “Tree Matching Applied to Vascular System,” in Graph-based Representation in Pattern Recognition, pp. 183–192, Springer, 2005. View at: Google Scholar
31. J. Tschirren, G. McLennan, K. Palagyi, E. Hoffman, and M. Sonka, “Matching and Anatomical Labeling of Human Airway Tree,” IEEE Transactions on Medical Imaging, vol. 24, no. 12, pp. 1540–1547, 2005. View at: Google Scholar
32. J. N. Kaftan, A. P. Kiraly, D. P. Naidich, and C. L. Novak, “A Novel Multipurpose Tree and Path Matching Algorithm with Application to Airway Trees,” Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 215–224, 2006. View at: Google Scholar
33. M. W. Graham and W. E. Higgins, “Globally Optimal Model-based Matching of Anatomical Trees,” in Proceedings of SPIE - Medical Imaging 2006: Image Processing, 614415, 2006. View at: Google Scholar
34. VOXEL-MAN Group, Voxel-Man Organ Atlas, University Medical Center Hamburg-Eppendorf, 2008.
35. C. Lorenz and J. v. Berg, “A Comprehensive Shape Model of the Heart,” Medical Image Analysis, vol. 10, no. 4, pp. 657–670, 2006. View at: Google Scholar
36. R. Whitaker and X. Xue, “Variable-Conductance, Level-Set Curvature for Image Denoising,” in Proceedings of the International Conference on Image Processing, vol. 3, pp. 142–145, 2001. View at: Google Scholar
37. T.-C. Lee, R. L. Kashyap, and C.-N. Chu, “Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms,” Graphical Models and Image Process (CVGIP), vol. 56, no. 6, pp. 462–478, 1994. View at: Google Scholar
38. Y. Chen, C. O. Laura, and K. Drechsler, “Generation of a Graph Representation from Threedimensional Skeletons of the Liver Vasculature,” in Proceedings of the 2nd International Conference on BioMedical Engineering and Informatics (BMEI), 2009. View at: Google Scholar

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