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Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas

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Abstract

Purpose

   Determination of acetabular cartilage loss in the hip joint is a clinically significant metric that requires image segmentation. A new semiautomatic method to segment acetabular cartilage in computed tomography (CT) arthrography scans was developed and tested.

Methods

   A semiautomatic segmentation method was developed based on the combination of anatomical and statistical information. Anatomical information is identified using the pelvic bone position and the contact area between cartilage and bone. Statistical information is acquired from CT intensity modeling of acetabular cartilage and adjacent tissue structures. This method was applied to the identification of acetabular cartilages in 37 intra-articular CT arthrography scans.

Results

The semiautomatic anatomical–statistical method performed better than other segmentation methods. The semiautomatic method was effective in noisy scans and was able to detect damaged cartilage.

Conclusions

   The new semiautomatic method segments acetabular cartilage by fully utilizing the statistical and anatomical information in CT arthrography datasets. This method for hip joint cartilage segmentation has potential for use in many clinical applications.

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References

  1. Cicuttini F, Forbes A, Morris K, Woodford N, Stuckey S (2000) Determining the volume of hip cartilage by magnetic resonance imaging. Radiography 6(2):79–82

    Article  Google Scholar 

  2. Lane NE (2007) Osteoarthritis of the hip. N Engl J Med 357(14):1413–1421

    Article  CAS  PubMed  Google Scholar 

  3. 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):650–657

    Article  PubMed  Google Scholar 

  4. 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 Carti 20(7):646–652

    Article  CAS  Google Scholar 

  5. Mechlenburg I, Nyengaard JR, Gelineck J, Soballe K (2007) Cartilage thickness in the hip joint measured by mri and stereology—a methodological study. Osteoarthr Cartil 15(4):366–371

    Article  CAS  PubMed  Google Scholar 

  6. Cheng Y, Wang S, Yamazaki T, Zhao J, Nakajima Y, Tamura S (2007) Hip cartilage thickness measurement accuracy improvement. Comput Med Imag Graph 31(8):643–655

    Article  Google Scholar 

  7. 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):2253–2263

  8. 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. Imag Sci 59(5):253–266

    Article  Google Scholar 

  9. Raynauld JP, Kauffmann C, Beaudoin G, Berthiaume MJ, de Guisei JA, Bloch DA, Camacho F, Godbout B et al (2003) Reliability of a quantification imaging system using magnetic resonance images to measure cartilage thickness and volume in human normal and osteoarthritic knees. Osteoarthr Cartil 11(5):351–360

    Article  PubMed  Google Scholar 

  10. 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):1541–1559

    Article  PubMed  Google Scholar 

  11. 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 10:536–543

  12. Solloway S, Hutchinson CE, Waterton JC, Taylor CJ (1997) The use of active shape models for making thickness measurements of articular cartilage from mr images. Magn Reson Med 37(6):943–952

    Article  CAS  PubMed  Google Scholar 

  13. Lee S, Park SH, Shim H, Yun ID, Lee SU (2011) Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images. Comput Vis Image Und 115(12):1710–1720

    Article  Google Scholar 

  14. 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 Imag 29(1):55–64

    Article  Google Scholar 

  15. 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 Im 31(10):1731–1743

  16. Yin Y, Zhang X, Williams R, Wu X, Anderson DD, Sonka M (2010) LOGISMOS-layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint. IEEE Trans Med Imaging 29(12):2023–2037

    Article  PubMed Central  PubMed  Google Scholar 

  17. Kauffmann C, Gravel P, Godbout B, Gravel A, Beaudoin G, Raynauld JP, Martel-Pelletier J, Pelletier JP, de Guise JA (2003) Computer-aided method for quantification of cartilage thickness and volume changes using mri: validation study using a synthetic model. IEEE Trans Biomed Eng 50(8):978–988

    Article  PubMed  Google Scholar 

  18. Tang J, Millington S, Acton ST, Crandall J, Hurwitz S (2006) Surface extraction and thickness measurement of the articular cartilage from mr images using directional gradient vector flow snakes. IEEE Trans Biomed Eng 53(5):896–907

    Article  PubMed  Google Scholar 

  19. Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2013) Abdominal multi-organ ct segmentation using organ correlation graph and prediction-based shape and location priors. Proc MICCAI 8151:275–282

    Google Scholar 

  20. Boykov YY, Jolly MP (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. Proc ICCV I:12–105

  21. 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):712–721

  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 12:811–818

    Google Scholar 

  23. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Sys Man Cyber 9(1):62–66

    Article  Google Scholar 

  24. 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 ACM SIGGRAPH, pp 67–76

  25. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):299–302

    Article  Google Scholar 

  26. Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C et al (2009) Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Trans Med Imaging 28(8):1251–1265

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Pooneh R. Tabrizi & Reza A. Zoroofi

  2. Graduate School of Engineering, Kobe University Rokkoudai, 1-1, Nada-ku, Kobe-shi, Hyogo, 657-8501, Japan

    Futoshi Yokota

  3. Graduate School of Medicine, Osaka University Yamadaoka, 2-2, Suita-shi, Osaka, 565-0871, Japan

    Satoru Tamura, Takashi Nishii & Yoshinobu Sato

Authors
  1. Pooneh R. Tabrizi
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  2. Reza A. Zoroofi
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  3. Futoshi Yokota
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  4. Satoru Tamura
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  5. Takashi Nishii
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  6. Yoshinobu Sato
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Correspondence to Pooneh R. Tabrizi.

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Tabrizi, P.R., Zoroofi, R.A., Yokota, F. et al. Acetabular cartilage segmentation in CT arthrography based on a bone-normalized probabilistic atlas. Int J CARS 10, 433–446 (2015). https://doi.org/10.1007/s11548-014-1101-1

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  • DOI: https://doi.org/10.1007/s11548-014-1101-1

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