Abstract
Automatic segmentation of femurs in clinical computed tomography remains a challenge. Joints degraded by old age are a particularly challenging dataset to segment. The objective of this study is to evaluate existing methods and propose an alternative method for segmentation of femurs in clinical computed tomography datasets for joints degraded by old age. Bilateral hip computed tomography scans of three cadaveric specimens (six femurs) were available for this study. Deformable registration using an affine selection criterion was used for atlas-based segmentation. For comparison, the six femurs were also segmented with two graph-cut algorithms. An automatic graph-cut segmentation algorithm was only able to separate the femur from the pelvis in two of the six femurs due to a limitation of graph-cuts. The atlas-based method produced consistent automatic segmentations for all degraded joints. In conclusion, atlas-based femur segmentation performs considerably better than an automatic graph-cut algorithm when applied to degraded joints.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pickhardt, P., Pooler, B., Lauder, T., del Rio, A., Bruce, R., Binkley, N.: Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann. Intern. Med. 158(8), 588–595 (2013)
Burge, R., Dawson-Hughes, B., Solomon, D., Wong, J., King, A., Tosteson, A.: Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J. Bone Miner. Res. 22(3), 465–475 (2007)
Zoroofi, R., Sato, Y., Sasama, T., Nishii, T., Sugano, N., Yonenobu, K., Yoshikawa, H., Ochi, T., Tamura, S.: Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Trans. Inf. Technol. Biomed. 7(4), 329–343 (2003)
Kang, Y., Engelke, K., Kalender, W.: A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Trans. Med. Imaging 22(5), 586–598 (2003)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vision 70(2), 109–131 (2006)
Pauchard, Y., Fitze, T., Browarnik, D., Eskandari, A., Pauchard, I., Enns-Bray, W., Pálsson, H., Sigurdsson, S., Ferguson, S., Harris, T., Gudnason, V., Helgason, B.: Interactive graph-cut segmentation for fast creation of finite element models from clinical CT data for hip fracture prediction. Comput. Methods Biomech. Biomed. Eng. 19(16), 1693–1703 (2016)
Krčah, M., Székely, G., Blanc, R.: Fully automatic and fast segmentation of the femur bone from 3D-CT images with no shape prior. In: Proceedings of the 8th IEEE International Symposium on Biomedical Imaging - ISBI 2011, pp. 2087–2090. IEEE (2011)
Descoteaux, M., Audette, M., Chinzei, K., Siddiqi, K.: Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery. Comput. Aided Surg. 11(5), 247–255 (2006)
Ehrhardt, J., Handels, H., Malina, T., Strathmann, B., Plötz, W., Pöppl, S.: Atlas-based segmentation of bone structures to support the virtual planning of hip operations. Int. J. Med. Inform. 64(2–3), 439–447 (2001)
Thirion, J.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)
Whitmarsh, T., Treece, G.M., Poole, K.E.S.: Automatic segmentation and discrimination of connected joint bones from CT by multi-atlas registration. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. LNCVB, vol. 17, pp. 199–207. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07269-2_17
Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)
Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)
Dice, L.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)
Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4), 1428–1442 (2004)
Acknowledgements
The authors would like to thank NSERC CGS-M for funding, the Body Donation Program at the Gross Anatomy Laboratory for access to cadaveric specimens, and the individuals who graciously contributed their bodies. The authors would also like to thank Dr. Sonny Chan from the Department of Computer Science at the University of Calgary for guidance.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Besler, B.A., Michalski, A.S., Forkert, N.D., Boyd, S.K. (2018). Automatic Full Femur Segmentation from Computed Tomography Datasets Using an Atlas-Based Approach. In: Glocker, B., Yao, J., Vrtovec, T., Frangi, A., Zheng, G. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science(), vol 10734. Springer, Cham. https://doi.org/10.1007/978-3-319-74113-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-74113-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74112-3
Online ISBN: 978-3-319-74113-0
eBook Packages: Computer ScienceComputer Science (R0)