Abstract
Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Botser, I.B., Jackson, T.J., Smith, T.W., Leonard, J.P., Stake, C.E., Domb, B.G.: Open surgical dislocation versus arthroscopic treatment of femoroacetabular impingement. Am. J. Orthop. (Belle Mead NJ) 43(5), 209–214 (2014)
Tannast, M., Kubiak-Langer, M., Langlotz, F., Puls, M., Murphy, S.B., Siebenrock, K.A.: Noninvasive three-dimensional assessment of femoroacetabular impingement. J. Orthop. Res. 25(1), 122–131 (2007)
Puls, M., Ecker, T.M., Tannast, M., Steppacher, S.D., Siebenrock, K.A., Kowal, J.H.: The equidistant method novel hip joint simulation algorithm for detection of femoroacetabular impingement. Comput. Aided Surg. 15(4–6), 75–82 (2010)
Yokota, F., Okada, T., Takao, M., Sugano, S., Tada, Y., Sato, Y.: Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 811–818. Springer, Heidelberg (2009)
Yokota, F., Okada, T., Takao, M., Sugano, N., Tada, Y., Tomiyama, N., Sato, Y.: Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 190–197. Springer, Heidelberg (2013)
Seim, H., Kainmueller, D., Heller, M., Lamecker, H., Zachow, S., Hege, H.-C.: Automatic segmentation of the pelvic bones from CT data based on a statistical shape model. In: Botha, C.P., Kindlmann, G., Niessen, W.J., Preim, B. (eds.) Eurographics Workshop on Visual Computing for Biomedicine, pp. 67–78. Delt, Netherlands (2008)
Kainmueller, D., Lamecker, H., Zachow, S., Hege, H.-C.: An articulated statistical shape model for accurate hip joint segmentation. In: IEEE EMBC 2009, pp. 6345–6351. Minneapolis, Minnesota, USA (2009)
Balestra, S., Schumann, S., Heverhagen, J., Nolte, L., Zheng, G.: Articulated statistical shape model-based 2D-3D reconstruction of a hip joint. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds.) IPCAI 2014. LNCS, vol. 8498, pp. 128–137. Springer, Heidelberg (2014)
Park, H., Bland, P., Meyer, C.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE TMI 22(4), 483–492 (2003)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
Lindner, C., Thiagarajah, S., Wilkinson, J.M., arcOGEN Consortium, Wallis, G., Cootes, T.F.: Fully automatic segmentation of the proximal femur using random forest regression voting. IEEE TMI 32(8), 1462–1472 (2013)
Yang, C., Duraiswami, R., Davis, L.: Efficient kernel machines using the improved fast Gauss transform. Adv. Neural Inf. Process. Syst. 17, 1561–1568 (2005)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731–741 (2008)
Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE TMI 32(9), 1723–1730 (2013)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23(11), 1222–1239 (2001)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), 61–72 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chu, C., Chen, C., Zheng, G. (2014). Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2014. Lecture Notes in Computer Science(), vol 8899. Springer, Cham. https://doi.org/10.1007/978-3-319-13410-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-13410-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13409-3
Online ISBN: 978-3-319-13410-9
eBook Packages: Computer ScienceComputer Science (R0)