Advertisement

Shape-based acetabular cartilage segmentation: application to CT and MRI datasets

  • Pooneh R. TabriziEmail author
  • Reza A. Zoroofi
  • Futoshi Yokota
  • Takashi Nishii
  • Yoshinobu Sato
Original Article

Abstract

Purpose

A new method for acetabular cartilage segmentation in both computed tomography (CT) arthrography and magnetic resonance imaging (MRI) datasets with leg tension is developed and tested.

Methods

The new segmentation method is based on the combination of shape and intensity information. Shape information is acquired according to the predictable nonlinear relationship between the U-shaped acetabulum region and acetabular cartilage. Intensity information is obtained from the acetabular cartilage region automatically to complete the segmentation procedures. This method is evaluated using 54 CT arthrography datasets with two different radiation doses and 20 MRI datasets. Additionally, the performance of this method in identifying acetabular cartilage is compared with four other acetabular cartilage segmentation methods.

Results

This method performed better than the comparison methods. Indeed, this method maintained good accuracy level for 74 datasets independent of the cartilage modality and with minimum user interaction in the bone segmentation procedures. In addition, this method was efficient in noisy conditions and in detection of the damaged cartilages with zero thickness, which confirmed its potential clinical usefulness.

Conclusions

Our new method proposes acetabular cartilage segmentation in three different datasets based on the combination of the shape and intensity information. This method executes well in situations where there are clear boundaries between the acetabular and femoral cartilages. However, the acetabular cartilage and pelvic bone information should be obtained from one dataset such as CT arthrography or MRI datasets with leg traction.

Keywords

Acetabular cartilage CT arthrography Graph-Cut  K-OPLS MRI Pelvic bone 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

For this type of study, formal consent is not required because this study is a retrospective study.

Informed consent

Written informed consent was not required for this study because this study is a retrospective study.

Supplementary material

11548_2015_1313_MOESM1_ESM.docx (1 mb)
Supplementary material 1 (docx 1041 KB)

References

  1. 1.
    Lane NE (2007) Osteoarthritis of the hip. N Engl J Med 357(14):1413–1421CrossRefPubMedGoogle Scholar
  2. 2.
    Nishii T, Sugano N, Sato Y, Tanaka H, Miki H, Yoshikawa H (2004) Three-dimensional distribution of acetabular cartilage thickness in patients with hip dysplasia: a fully automated computational analysis of mr imaging. Osteoarthr Cartil 12(8):650–657CrossRefPubMedGoogle Scholar
  3. 3.
    Tamura S, Nishii T, Shiomi T, Yamazaki Y, Murase K, Yoshikawa H, Sugano N (2012) Three-dimensional patterns of early acetabular cartilage damage in hip dysplasia; a high-resolutional CT arthrography study. Osteoarthr Cartil 20(7):646–652CrossRefPubMedGoogle Scholar
  4. 4.
    Williams TG, Holmes AP, Waterton JC, Maciewicz RA, Hutchinson CE, Moots RJ, Nash AFP, Taylor CJ (2010) Anatomically corresponded regional analysis of cartilage in asymptomatic and osteoarthritic knees by statistical shape modelling of the bone. IEEE Trans Med Imaging 29(8):1541–1559CrossRefPubMedGoogle Scholar
  5. 5.
    Cheng Y, Wang S, Yamazaki T, Zhaob J, Nakajima Y, Tamura S (2007) Hip cartilage thickness measurement accuracy improvement. Comput Med Imaging Graph 31(8):643–655CrossRefPubMedGoogle Scholar
  6. 6.
    Siversson C, Akhondi-Asl A, Bixby S, Kim YJ, Warfield SK (2014) Three-dimensional hip cartilage quality assessment of morphology and dGEMRIC by planar maps and automated segmentation. Osteoarthr Cartil 22(10):1511–1515CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Tabrizi PR, Zoroofi RA, Yokota F, Tamura S, Nishii T, Sato Y (2015) Acetabular cartilage segmentation based on bone-normalized probabilistic atlas from contrast-enhanced CT images. Int J Comput Assist Radiol Surg 10(4):433–446CrossRefPubMedGoogle Scholar
  8. 8.
    Cheong J, Suter D, Cicuttini F (2005) Development of semi-automatic segmentation methods for measuring tibial cartilage volume. In: Proceedings of DICTA, pp 307–314Google Scholar
  9. 9.
    Fripp J, Crozier S, Warfield SK, Ourselin S (2010) Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans Med Imaging 29(1):55–64CrossRefPubMedGoogle Scholar
  10. 10.
    Glocker B, Komodakis N, Paragios N, Glaser C, Tziritas G, Navab N (2007) Primal/dual linear programming and statistical atlases for cartilage segmentation. Proc MICCAI 10:536–543Google Scholar
  11. 11.
    Folkesson J, Dam E, Olsen O, Pettersen P, Christiansen C (2007) Segmenting articular cartilage automatically using a voxel classification approach. IEEE Trans Med Imaging 26(1):106–115CrossRefPubMedGoogle Scholar
  12. 12.
    Lee S, Park S, Shim H, Yun I, Lee S (2011) Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images. Comput Vis Image Underst 115(12):1710–1720CrossRefGoogle Scholar
  13. 13.
    Zhang K, Lu W, Marziliano P (2013) Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies. Magn Reson Imaging 31(10):1731–1743CrossRefPubMedGoogle Scholar
  14. 14.
    Baniasadipour A, Zoroofi RA, Sato Y, Nishii T, Tanaka H (2011) Automated knowledge-based segmentation and analysis of the hip bones and cartilages using multi-slice CT data. Imaging Sci 59(5):253–266CrossRefGoogle Scholar
  15. 15.
    Khanmohammadi M, Zoroofi RA, Nishii T, Tanaka H, Sato Y (2009) A hybrid technique for thickness-map visualization of the hip cartilages in MRI. IEICE Trans Inf Syst E92-D(11):2253–2263Google Scholar
  16. 16.
    Du X, Velut J, Bolbos R, Beuf O, Odet C, Benoit-Cattin H (2008) 3-D knee cartilage segmentation using a smoothing b-spline active surface. In: Proceedings of ICIP, pp 2924–2927Google Scholar
  17. 17.
    Ali-Shah SA, Yahya K, Mubashar G, Bais A (2010) Quantification and visualization of MRI cartilage of the knee: a simplified approach. In: Proceedings of ICET, pp 175–180Google Scholar
  18. 18.
    Rantalainen M, Bylesjo M, Cloarec O, Nicholson JK, Holmes E, Trygg J (2007) Kernel-based orthogonal projections to latent structures (K-OPLS). J Chemom 21(7–9):376–385CrossRefGoogle Scholar
  19. 19.
    Bylesjo M, Rantalainen M, Nicholson JK, Holmes E, Trygg J (2008) K-OPLS package: kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space. BMC Bioinform 9(106):1–7Google Scholar
  20. 20.
    Fonville JM, Bylesjö M, Coen M, Nicholson JK, Holmes E, Lindon JC, Rantalainen M (2011) Non-linear modeling of 1h NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing. Anal Chim Acta 705(1–2):72–80CrossRefPubMedGoogle Scholar
  21. 21.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):299–302CrossRefGoogle Scholar
  22. 22.
    Yokota F, Okada T, Takao M, Sugano N, Tada Y, Sato Y (2009) Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure. Proc MICCAI 12:811–818Google Scholar
  23. 23.
    National Library of Medicine Insight Segmentation and Registration Toolkit. http://www.itk.org
  24. 24.
    Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. Trans Med Imaging 18(8):712–721CrossRefGoogle Scholar
  25. 25.
    Carr JC, Beatson RK, Cherrie JB, Mitchell TJ, Fright WR, McCallum BC, Evans TR (2001) Reconstruction and representation of 3D objects with radial basis functions. In: Proceedings of SIGGRAPH, pp 67–76Google Scholar
  26. 26.
    Zhang N, Zhang J, Shi R (2008) An improved Chan-Vese model for medical image segmentation. Proc Comput Sci Softw Eng 1:864–867Google Scholar
  27. 27.
    Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmüller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Nemeth G, Raicu DS, Rau AM, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Pooneh R. Tabrizi
    • 1
    Email author
  • Reza A. Zoroofi
    • 1
  • Futoshi Yokota
    • 2
  • Takashi Nishii
    • 3
  • Yoshinobu Sato
    • 2
  1. 1.Control and Intelligent Processing Center of Excellence, School of Electrical and Computer EngineeringUniversity of TehranTehranIran
  2. 2.Imaging-Based Computational Biomedicine (ICB) Lab, Graduate School of Information ScienceNara Institute of Science and Technology (NAIST)OsakaJapan
  3. 3.Graduate School of MedicineOsaka UniversitySuita-shiJapan

Personalised recommendations