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Semiautomatic classification of acetabular shape from three-dimensional ultrasound for diagnosis of infant hip dysplasia using geometric features

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Developmental dysplasia of the hip (DDH) is a congenital deformity which in severe cases leads to hip dislocation and in milder cases to premature osteoarthritis. Image-aided diagnosis of DDH is partly based on Graf classification which quantifies the acetabular shape seen at two-dimensional ultrasound (2DUS), which leads to high inter-scan variance. 3D ultrasound (3DUS) is a promising alternative for more reliable DDH diagnosis. However, manual quantification of acetabular shape from 3DUS is cumbersome.

Methods

Here, we (1) propose a semiautomated segmentation algorithm to delineate 3D acetabular surface models from 3DUS using graph search; (2) propose a fully automated method to classify acetabular shape based on a random forest (RF) classifier using features derived from 3D acetabular surface models; and (3) test diagnostic accuracy on a dataset of 79 3DUS infant hip recordings (36 normal, 16 borderline, 27 dysplastic based on orthopedic surgeon assessment) in 42 patients. For each 3DUS, we performed semiautomated segmentation to produce 3D acetabular surface models and then calculated geometric features including the automatic \(\mathrm{a}\)lpha (AA), acetabular contact angle (ACA), kurtosis (K), skewness (S) and convexity (C). Mean values of features obtained from surface models were used as inputs to train a RF classifier.

Results

Surface models were generated rapidly (user time 46.2 s) via semiautomated segmentation and visually closely correlated with actual acetabular contours (RMS error 1.39 ± 0.7 mm). A paired nonparametric u test on of feature values in each category showed statistically significant variation (p < 0.001) for AA, ACA and convexity. The RF classifier was 100 % specific and 97.2 % sensitive in classifying normal versus dysplastic hips and yielded true positive rates of 94.4, 62.5 and 89.9 % for normal, borderline and dysplastic hips.

Conclusions

The proposed technique reduces the subjectivity of image-aided DDH diagnosis and could be useful in clinical practice.

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References

  1. Furnes O, Lie S, Espehaug B, Vollset S, Engesaeter L, Havelin L (2001) Hip disease and the prognosis of total hip replacements—a review of 53 698 primary total hip replacements reported to the Norwegian arthroplasty register 1987–99. J Bone Jt Surg Br 83(4):579–586

  2. Dezateux C, Rosendahl K (2007) Developmental dysplasia of the hip. Lancet 369(9572):1541–1552

    Article  PubMed  Google Scholar 

  3. Bache CE, Clegg J, Herron M (2002) Risk factors for developmental dysplasia of the hip: ultrasonographic findings in the neonatal period. J Pediatr Orthop B 11(3):212–218

    PubMed  Google Scholar 

  4. Clarke N, Clegg J, Al-Chalabi A (1989) Ultrasound screening of hips at risk for CDH. Failure to reduce the incidence of late cases. J Bone Jt Surg Br 71(1):9–12

    CAS  Google Scholar 

  5. Graf R (1984) Fundamentals of sonographic diagnosis of infant hip dysplasia. J Pediatr Orthop 4(6):735–740

    Article  CAS  PubMed  Google Scholar 

  6. Mabee M, Dulai S, Thompson RB and Jaremko JL (2014) Reproducibility of acetabular landmarks and a standardized coordinate system obtained from 3D hip ultrasound. Ultrason Imaging 37(4):267–276. doi:10.1177/0161734614558278

  7. Jaremko JL, Mabee M, Swami VG, Jamieson L, Chow K, Thompson RB (2014) Potential for change in US diagnosis of hip dysplasia solely caused by changes in probe orientation: patterns of alpha-angle variation revealed by using three-dimensional US. Radiology 273(3):870–878

    Article  PubMed  Google Scholar 

  8. Cheng E, Mabee M, Swami VG, Pi Y, Thompson R, Dulai S, Jaremko JL (2015) Ultrasound quantification of acetabular rounding in hip dysplasia: reliability and correlation to treatment decisions in a retrospective study. Ultrasound Med Biol 41(1):56–63

    Article  PubMed  Google Scholar 

  9. Yavuz OY, Uras I, Tasbas BA, Ozdemir MH, Kaya M, Komurcu M (2014) A new measurement method in Graf technique: Prediction of future acetabular development is possible in physiologically immature hips. J Pediatr Orthop 34(6):591–596

    PubMed  Google Scholar 

  10. Hareendranathan AR, Mabee M, Punithakumar K, Noga M, Jaremko JL (2016) Toward automated classification of acetabular shape in ultrasound for diagnosis of DDH: contour \(\alpha \) and the rounding index. Comput Methods Programs Biomed 30(129):89–98

    Article  Google Scholar 

  11. Mabee MG, Hareendranathan AR, Thompson RB, Dulai S, Jaremko JL (2016) An index for diagnosing infant hip dysplasia using 3-D ultrasound: the ACA. Pediatr Radiol 11:1–9

    Google Scholar 

  12. Zoroofi R, Sato Y, Sasama T, Nishii T, Sugano N, Yonenobu K, Yoshikawa H, Ochi T, Tamura S et al (2003) Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Trans Inf Technol Biomed 7(4):329–343

    Article  PubMed  Google Scholar 

  13. Kainmueller D, Lamecker H, Zachow S and Hege H-C (2009) An articulated statistical shape model for accurate hip joint segmentation. In: Annual international conference of the IEEE engineering in medicine and biology society, 2009. EMBC 2009

  14. Gilles B, Moccozet L, Magnenat-Thalmann N (2006) Anatomical modelling of the musculoskeletal system from MRI. In: Larsen R, Nielsen M, Sporring J (eds) Medical image computing and computer-assisted intervention-MICCAI. Springer, Berlin, pp 289–296

    Google Scholar 

  15. Beek M, Abolmaesumi P, Luenam S, Ellis RE, Sellens RW, Pichora DR (2008) Validation of a new surgical procedure for percutaneous scaphoid fixation using intra-operative ultrasound. Med Imag Anal 12(2):152–162

  16. Jain AK, Taylor RH (2004) Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a bayesian probabilistic framework. In: Medical imaging 2004. International Society for Optics and Photonics, pp 131–142

  17. Kowal J, Amstutz C, Langlotz F, Talib H, Ballester MG (2007) Automated bone contour detection in ultrasound B-mode images for minimally invasive registration in computer-assisted surgery—an in vitro evaluation. Int J Med Robot Comput Assist Surg 3(4):341–348

    Article  Google Scholar 

  18. Hacihaliloglu I, Abugharbieh R, Hodgson AJ, Rohling RN, Guy P (2012) Automatic bone localization and fracture detection from volumetric ultrasound images using 3-D local phase features. Ultrasound Med Biol 38(1):128–144

    Article  PubMed  Google Scholar 

  19. Quader N, Hodgson A, Mulpuri K, Savage T, Abugharbieh R (2015) Automatic assessment of developmental dysplasia of the hip. In: IEEE 12th international symposium on biomedical imaging (ISBI) 2015, pp 13–16, 16–19

  20. Luis-Garcia D, Alberola-Lopez C et al. (2006) Parametric 3D hip joint segmentation for the diagnosis of developmental dysplasia. In: 28th Annual international conference of the IEEE engineering in medicine and biology society, 2006. EMBS’06

  21. Abhilash RH, Mabee M, Punithakumar K, Noga M and Jaremko JL (2015) A technique for semi-automatic segmentation of echogenic structures in 3DUS, applied to infant hip dysplasia. Int J Comput Assist Radiol Surg 11(1):31–42

  22. Mortensen EN, Barrett WA (1998) Interactive segmentation with intelligent scissors. Gr Models Imag Process 60(5):349–384

    Article  Google Scholar 

  23. Hamarneh G, Yang J, McIntosh C, Langille M (2005) 3D live-wire-based semi-automatic segmentation of medical images. In: Medical imaging 2005. International Society for Optics and Photonics, pp 1597–1603

  24. Falcao AX, Udupa JK (1997) Segmentation of 3D objects using live wire. In: Medical imaging 1997. International Society for Optics and Photonics, pp 228–235

  25. Li K, Wu X, Chen DZ, Sonka M (2006) Optimal surface segmentation in volumetric images-a graph-theoretic approach. IEEE Trans Pattern Anal Mach Intell 28(1):119–134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271

    Article  Google Scholar 

  27. Breiman L (2001) Random forests. Mach learn 45(1):5–32

    Article  Google Scholar 

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Acknowledgements

We thank Dr. Pierre Boulanger (Professor, Department of Computing Science) and Dr. Richard Thompson (Associate Professor, Department of Biomedical Engineering) for their insight and expertise that greatly helped in this research.

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Authors and Affiliations

  1. Servier Virtual Cardiac Centre and Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, T6G 2B7, Canada

    Abhilash Rakkunedeth Hareendranathan, Dornoosh Zonoobi, Myles Mabee, Chad Diederichs, Kumaradevan Punithakumar, Michelle Noga & Jacob L. Jaremko

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  1. Abhilash Rakkunedeth Hareendranathan
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  2. Dornoosh Zonoobi
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  3. Myles Mabee
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  4. Chad Diederichs
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  5. Kumaradevan Punithakumar
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  6. Michelle Noga
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  7. Jacob L. Jaremko
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Corresponding author

Correspondence to Abhilash Rakkunedeth Hareendranathan.

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Funding

This work was funded in part by the CIHR– Sick Kids Foundation New Investigator Research Grant 201401SKF and Capital Health Endowment in Diagnostic Radiology, Radiologic Society of North America (RSNA) Research Seed Grant and Servier Canada

Conflict of interest

The authors Abhilash Rakkunedeth Hareendranathan, Dornoosh Zonoobi, Myles Mabee, Chad Diederichs, Kumaradevan Punithakumar, Michelle Noga and Jacob L. Jaremko declare that they have no conflict of interest

Ethical approval

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

Informed consent

Informed consent was obtained from all patients for being included in the study

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Hareendranathan, A.R., Zonoobi, D., Mabee, M. et al. Semiautomatic classification of acetabular shape from three-dimensional ultrasound for diagnosis of infant hip dysplasia using geometric features. Int J CARS 12, 439–447 (2017). https://doi.org/10.1007/s11548-016-1510-4

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  • DOI: https://doi.org/10.1007/s11548-016-1510-4

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