Abstract
Automatic extraction of surface models of both pelvis and proximal femur of a hip joint from 3D CT images is an important and challenging task for computer assisted diagnosis and planning of periacetabular osteotomy (PAO). Due to the narrowness of hip joint space, the adjacent surfaces of the acetabulum and the femoral head are hardly distinguishable from each other in the target CT images. This paper presents a fully automatic method for segmenting hip CT images using random forest (RF) regression-based atlas selection and optimal graph search-based surface detection. The two fundamental contributions of our method are: (1) An efficient RF regression framework is developed for a fast and accurate landmark detection from the hip CT images. The detected landmarks allow for not only a robust and accurate initialization of the atlases within the target image space but also an effective selection of a subset of atlases for a fast atlas-based segmentation; and (2) 3-D graph theory-based optimal surface detection is used to refine the extraction of the surfaces of the acetabulum and the femoral head with the ultimate goal to preserve hip joint structure and to avoid penetration between the two extracted surfaces. Validation on 30 hip CT images shows that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur with an average accuracy of 0.56 mm, 0.61 mm, and 0.57 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
Similar content being viewed by others
References
Ganz, R., Klaue, K., Vinh, T., Mast, J.: A new periacetabular osteotomy for the treatment of hip dysplasia: technique and preliminary results. Clin. Orthop. 232, 26–36 (1988)
Yokota, F., Okada, T., Takao, M., Sugano, N., 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: Eurographics Workshop on Visual Computing for Biomedicine, pp. 67–78 (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 (2009)
Ehrhardte, J., Handels, H., Plotz, W., Poppl, S.J.: Atlas-based recognition of anatomical structures and landmarks and the automatic computation of orthopedic parameters. Methods Inf. Med. 43, 391–397 (2004)
Pettersson, J., Knutsson, H., Borga., M.: Automatic hip bone segmentation using non-rigid registration. In: ICPR 2006 (2006)
Ying, X., Jurgen, F., Shekhar, S., Raphael, S., Craig, E., Stuart, C.: Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys. Med. Biol. 58, 7375–7390 (2013)
Lamecker, H., Seeba, M., Hege, H.C., Deuflhard, P.: A 3D statistical shape model of the pelvic bone for segmentation. In: SPIE, vol. 5370, pp. 1341–1351 (2004)
Kainmueller, D., Lamecker, H., Zachow, S., Hege, H.-C.: coupling deformable models for multi-object segmentation. In: Bello, F., Edwards, E. (eds.) ISBMS 2008. LNCS, vol. 5104, pp. 69–78. Springer, Heidelberg (2008)
Li, K., Wu, X., Chen, D., Sonka, M.: Optimal surface segmentation in volumetric images - a graph-theoretic approach. IEEE Trans. Pattern Anal. Mach. Itell. 28, 119–134 (2006)
Song, Q., Wu, X., Liu, Y., Smith, M., Buatti, J., Sonka, M.: Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 827–835. Springer, Heidelberg (2009)
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, 1462–1472 (2013)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Yang, C., Duraiswami, R., Davis, L.: Efficient kernel machines using the improved fast gauss transform. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1561–1568 (2005)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, vol. I, pp. 511–518 (2001)
Glocker, B., Komodakis, M., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through mrfs and efficient linear programming. Med. Image Anal. 12, 731–741 (2008)
Chu, C., et al.: 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)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23, 1222–1239 (2001)
Liu, L., Ecker, T., Schumann, S., Siebenrock, K., Nolte, L., Zheng, G.: Computer assisted planning and navigation of periacetabular osteotomy with range of motion optimization. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 643–650. Springer, Heidelberg (2014)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. PAMI 26, 147–159 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chu, C., Bai, J., Liu, L., Wu, X., Zheng, G. (2015). Fully Automatic Segmentation of Hip CT Images via Random Forest Regression-Based Atlas Selection and Optimal Graph Search-Based Surface Detection. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-16811-1_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16810-4
Online ISBN: 978-3-319-16811-1
eBook Packages: Computer ScienceComputer Science (R0)