A 3D Femoral Head Coverage Metric for Enhanced Reliability in Diagnosing Hip Dysplasia

  • Niamul QuaderEmail author
  • Antony J. Hodgson
  • Kishore Mulpuri
  • Anthony Cooper
  • Rafeef Abugharbieh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


Developmental dysplasia of the hip (DDH) in infancy refers to hip joint abnormalities ranging from mild acetabular dysplasia to irreducible femoral head dislocations. While 2D B-mode ultrasound (US) is currently used clinically to estimate the severity of femoral head subluxation in infant hips, such estimates suffer from high inter-exam variability. We propose using a novel 3D US-derived dysplasia metric, the 3D femoral head coverage (\(FHC_{3D}\)), which characterizes the 3D morphology of the femoral head relative to the vertical cortex of the ilium in an infants hip joint. We compute our 3D dysplasia metric by segmenting the femoral head using a voxel-wise probability map based on a tomographic reconstruction of 2D cross-sections each labeled with a probability score of that slice containing the femoral head. Using a dataset of 20 patient hip examinations, we demonstrate that our reconstructed femoral heads agree reasonably well with manually segmented femoral heads (mean dice coefficient of 0.71), with a significant reduction in variability of the associated metric relative to the existing manual 2D-based FHC ratio (\({\sim } 20\%\) reduction, \(p<0.05\)). Our findings suggest that the proposed 3D dysplasia metric may be more reliable than the conventional 2D metric, which may lead to a more reproducible test for diagnosing DDH.


  1. 1.
    Shorter, D., Hong, T., Osborn, D.A.: Cochrane review: Screening programmes for developmental dysplasia of the hip in newborn infants. Evid. Based Child Health Cochrane Rev. J. 8(1), 11–54 (2013)CrossRefGoogle Scholar
  2. 2.
    Gulati, V., Eseonu, K., Sayani, J., Ismail, N., Uzoigwe, C., Choudhury, M.Z., Gulati, P., Aqil, A., Tibrewal, S.: Developmental dysplasia of the hip in the newborn: a systematic review. World J. Orthop. 4(2), 32–41 (2013)CrossRefGoogle Scholar
  3. 3.
    Price, C.T., Ramo, B.A.: Prevention of hip dysplasia in children and adults. Orthop. Clin. North Am. 43(3), 269–279 (2012)CrossRefGoogle Scholar
  4. 4.
    American College of Radiology: ACR-AIUM practice guideline for the performance of the ultrasound examination for detection and assessment of developmental dysplasia of the hip (ACR guidelines) (2012)Google Scholar
  5. 5.
    Peterlein, C.D., Schüttler, K.F., Lakemeier, S., Timmesfeld, N., Görg, C., Fuchs-Winkelmann, S., Schofer, M.D.: Reproducibility of different screening classifications in ultrasonography of the newborn hip. BMC Pediatrics 10(1), 98 (2010)CrossRefGoogle Scholar
  6. 6.
    Hareendranathan, A.R., Mabee, M., Punithakumar, K., Noga, M., Jaremko, J.L.: Toward automated classification of acetabular shape in ultrasound for diagnosis of DDH: contour alpha angle and the rounding index. Comput. Methods Program. Biomed. 129, 89–98 (2016)CrossRefGoogle Scholar
  7. 7.
    Quader, N., Hodgson, A., Mulpuri, K., Cooper, A., Abugharbieh, R.: Towards reliable automatic characterization of neonatal hip dysplasia from 3D ultrasound images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 602–609. Springer, Cham (2016). doi: 10.1007/978-3-319-46720-7_70 CrossRefGoogle Scholar
  8. 8.
    de Luis-Garcia, R., Alberola-Lopez, C.: 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, EMBS 2006, pp. 4807–4810. IEEE (2006)Google Scholar
  9. 9.
    Graf, R.: Fundamentals of sonographic diagnosis of infant hip dysplasia. J. Pediatric Orthop. 4(6), 735–740 (1984)CrossRefGoogle Scholar
  10. 10.
    Kovesi, P., et al.: Symmetry and asymmetry from local phase. In: Tenth Australian Joint Conference on Artificial Intelligence, vol. 190, pp. 2–4. Citeseer (1997)Google Scholar
  11. 11.
    Quader, N., Hodgson, A., Abugharbieh, R.: Confidence weighted local phase features for robust bone surface segmentation in ultrasound. In: Linguraru, M.G., Oyarzun Laura, C., Shekhar, R., Wesarg, S., González Ballester, M.Á., Drechsler, K., Sato, Y., Erdt, M. (eds.) CLIP 2014. LNCS, vol. 8680, pp. 76–83. Springer, Cham (2014). doi: 10.1007/978-3-319-13909-8_10 Google Scholar
  12. 12.
    Descoteaux, M., Audette, M., Chinzei, K., Siddiqi, K.: Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery. Compu. Aided Surg. 11(5), 247–255 (2006)CrossRefGoogle Scholar
  13. 13.
    Torr, P.H., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78(1), 138–156 (2000)CrossRefGoogle Scholar
  14. 14.
    Quader, N., Hodgson, A.J., Mulpuri, K., Schaeffer, E., Abugharbieh, R.: Automatic evaluation of scan adequacy and dysplasia metrics in 2-D ultrasound images of the neonatal hip. Ultrasound Med. Biol. 43, 1252–1262 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Niamul Quader
    • 1
    Email author
  • Antony J. Hodgson
    • 1
  • Kishore Mulpuri
    • 1
  • Anthony Cooper
    • 1
  • Rafeef Abugharbieh
    • 1
  1. 1.BiSICLUniversity of British ColumbiaVancouverCanada

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