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Fast Segmentation of Abdominal Wall: Application to Sliding Effect Removal for Non-rigid Registration

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Abdominal Imaging. Computational and Clinical Applications (ABD-MICCAI 2012)

Abstract

The non-rigid registration of abdominal images is still a big challenge due to the breathing motion. Indeed, the sliding between the abdominal wall and the abdominal viscera makes the local deformation field discontinuous; it means that the classical registration approach, which assumes a smooth global deformation field cannot provide accurate and clinical-required results. Other new approaches intend to add in regularization a term to allow discontinuous deformation field near sliding boundary, however, the performance of such approaches needs to be further evaluated. We propose a new approach to perform abdominal image registration including a priori knowledge of the sliding area. Our strategy is to firstly delineate the abdominal wall in source and target images and create new images containing viscera only. Then a state-of-the-art non-rigid registration algorithm is adopted for the registration of the viscera region. In this paper, we firstly show why and how a quick interactive delineation of the full abdominal wall (AW) can be performed using B-spline interpolation. Secondly, we evaluate our registration approach on arterial and venous phase CT images. The results of our approach are compared to the one obtained using the same algorithm with the same parameters on the original data (without segmentation). The registration errors (mean ± SD) with our approach are: liver (1.94 ± 2.76 mm), left kidney (0.38 ± 0.66 mm), right kidney (0.42 ± 0.82 mm), spleen (4.15 ± 3.68 mm), which is much better than the registration result without segmentation: liver (6.48 ± 10.00 mm), left kidney (3.14 ± 3.39 mm), right kidney (2.79 ± 3.12 mm), spleen (17.45 ± 12.39 mm). The results clearly demonstrate our approach is a promising method to remove the sliding motion effect on the non-rigid registration of abdominal images.

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References

  1. Kiriyanthan, S., Fundana, K., Cattin, P.C.: Discontinuity Preserving Registration of Abdominal MR Images with Apparent Sliding Organ Motion. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging 2011. LNCS, vol. 7029, pp. 231–239. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Non-Rigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)

    Article  Google Scholar 

  3. Rohlfing, T., Maurer Jr., C.R., O’Dell, W.G., Zhong, J.: Modeling Liver Motion and Deformation during the Respiratory Cycle Using Intensity-Based Free-Form Registration of Gated MR Images. In: Medical Imaging: Visualization, Displaying, and Image-Guided Procedures. SPIE, vol. 4319, pp. 337–348 (2001)

    Google Scholar 

  4. Lee, W.C., Tublin, M., Chapman, B.: Registration of MR and CT Images of the Liver: Comparison of Voxel Similarity and Surface Based Registration Algorithms. Comput. Meth. Prog. Bio. 78, 101–114 (2005)

    Article  Google Scholar 

  5. Pace, D.F., Enquobahrie, A., Yang, H., Aylward, S.R., Niethammer, M.: Deformable Image Registration of Sliding Organs Using Anisotropic Diffusive Regularization. In: 8th IEEE International Symposium on Biomedical Imaging, pp. 407–413. IEEE Press, New York (2011)

    Google Scholar 

  6. Pace, D.F., Niethammer, M., Aylward, S.R.: Sliding Geometries in Deformable Image Registration. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging 2011. LNCS, vol. 7029, pp. 141–148. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Freiman, M., Voss, S., Warfield, S.: Demons Registration with Local Affine Adaptive Regularization: Application to Registration of Abdominal Structures. In: 8th IEEE International Symposium on Biomedical Imaging, pp. 1219–1222. IEEE Press, New York (2011)

    Google Scholar 

  8. Cahill, N.D., Noble, J.A., Hawkes, D.J.: A Demons Algorithm for Image Registration with Locally Adaptive Regularization. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 574–581. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Freiman, M., Voss, S.D., Warfield, S.K.: Abdominal Images Non-rigid Registration Using Local-Affine Diffeomorphic Demons. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging 2011. LNCS, vol. 7029, pp. 116–124. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  10. Zitova, B., Flusser, J.: Image Registration Methods: A Survey. Image Vision Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  11. Lee, S., Wolberg, G., Shin, S.Y.: Scattered Data Interpolation with Multilevel B-Splines. IEEE Trans. Vis. Comput. Graph. 3, 228–244 (1997)

    Article  Google Scholar 

  12. Bardinet, E., Cohen, L.D., Ayache, N.: Tracking and Motion Analysis of the Left Ventricle with Deformable Superquadrics. Med. Image Anal. 1(2), 129–149 (1996)

    Article  Google Scholar 

  13. Lee, S., Wolberg, G., Chwa, K.Y., Shin, S.Y.: Image Metamorphosis with Scattered Feature Constraints. IEEE Trans. Vis. Comput. Graph. 2, 337–354 (1996)

    Article  Google Scholar 

  14. Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Seutens, P., Mar, G.: Automated Multimodality Image Registration Using Information Theory. In: 14th International Conference on Information Processing in Medical Imaging, IPMI 1995, pp. 263–274 (1995)

    Google Scholar 

  15. Viola, P., Wells, W.M.: Alignment by Maximization of Mutual Information. Int. J. Comput. Vis. 24, 137–154 (1997)

    Article  Google Scholar 

  16. Insight Segmentation and Registration Toolkit (ITK), http://www.itk.org

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Zhu, W., Nicolau, S., Soler, L., Hostettler, A., Marescaux, J., Rémond, Y. (2012). Fast Segmentation of Abdominal Wall: Application to Sliding Effect Removal for Non-rigid Registration. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2012. Lecture Notes in Computer Science, vol 7601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33612-6_21

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  • DOI: https://doi.org/10.1007/978-3-642-33612-6_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33611-9

  • Online ISBN: 978-3-642-33612-6

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