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Automatic Landmark Estimation for Adolescent Idiopathic Scoliosis Assessment Using BoostNet

  • Hongbo Wu
  • Chris Bailey
  • Parham Rasoulinejad
  • Shuo LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Adolescent Idiopathic Scoliosis (AIS) exhibits as an abnormal curvature of the spine in teens. Conventional radiographic assessment of scoliosis is unreliable due to the need for manual intervention from clinicians as well as high variability in images. Current methods for automatic scoliosis assessment are not robust due to reliance on segmentation or feature engineering. We propose a novel framework for automated landmark estimation for AIS assessment by leveraging the strength of our newly designed BoostNet, which creatively integrates the robust feature extraction capabilities of Convolutional Neural Networks (ConvNet) with statistical methodologies to adapt to the variability in X-ray images. In contrast to traditional ConvNets, our BoostNet introduces two novel concepts: (1) a BoostLayer for robust discriminatory feature embedding by removing outlier features, which essentially minimizes the intra-class variance of the feature space and (2) a spinal structured multi-output regression layer for compact modelling of landmark coordinate correlation. The BoostNet architecture estimates required spinal landmarks within a mean squared error (MSE) rate of 0.00068 in 431 crossvalidation images and 0.0046 in 50 test images, demonstrating its potential for robust automated scoliosis assessment in the clinical setting.

Keywords

Boosting ConvNet AIS Scoliosis Deep Learning Outlier 

References

  1. 1.
    Weinstein, S.L., Dolan, L.A., Cheng, J.C., Danielsson, A., Morcuende, J.A.: Adolescent idiopathic scoliosis. Lancet 371(9623), 1527–1537 (2008)CrossRefGoogle Scholar
  2. 2.
    Asher, M.A., Burton, D.C.: Adolescent idiopathic scoliosis: natural history and long term treatment effects. Scoliosis 1(1), 2 (2006)CrossRefGoogle Scholar
  3. 3.
    Vrtovec, T., Pernuš, F., Likar, B.: A review of methods for quantitative evaluation of spinal curvature. Eur. Spine J. 18(5), 593–607 (2009)CrossRefGoogle Scholar
  4. 4.
    Anitha, H., Prabhu, G.: Automatic quantification of spinal curvature in scoliotic radiograph using image processing. J. Med. Syst. 36(3), 1943–1951 (2012)CrossRefGoogle Scholar
  5. 5.
    Anitha, H., Karunakar, A., Dinesh, K.: Automatic extraction of vertebral endplates from scoliotic radiographs using customized filter. Biomed. Eng. Lett. 4(2), 158–165 (2014)CrossRefGoogle Scholar
  6. 6.
    Sardjono, T.A., Wilkinson, M.H., Veldhuizen, A.G., van Ooijen, P.M., Purnama, K.E., Verkerke, G.J.: Automatic cobb angle determination from radiographic images. Spine 38(20), 1256–1262 (2013)CrossRefGoogle Scholar
  7. 7.
    Sánchez-Fernández, M., de Prado-Cumplido, M., Arenas-García, J., Pérez-Cruz, F.: SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Trans. Signal Process. 52(8), 2298–2307 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Zhen, X., Wang, Z., Islam, A., Bhaduri, M., Chan, I., Li, S.: Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation. Med. Image Anal. 30, 120–129 (2016)CrossRefGoogle Scholar
  9. 9.
    Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mrida, A., Snchez, C.I., Mann, R., den Heeten, A., Karssemeijer, N.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)CrossRefGoogle Scholar
  10. 10.
    Christ, P.F., Elshaer, M.E.A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., D’Anastasi, M., Sommer, W.H., Ahmadi, S., Menze, B.H.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. CoRR abs/1610.02177Google Scholar
  11. 11.
    Acuña, E., Rodriguez, C.: On detection of outliers and their effect in supervised classification (2004)Google Scholar
  12. 12.
    Sun, H., Zhen, X., Bailey, C., Rasoulinejad, P., Yin, Y., Li, S.: Direct estimation of spinal cobb angles by structured multi-output regression. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T., Shen, D. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 529–540. Springer, Cham (2017). doi: 10.1007/978-3-319-59050-9_42 CrossRefGoogle Scholar
  13. 13.
    Chollet, F., Keras: (2015). https://github.com/fchollet/keras
  14. 14.
    S.D.S. Group: Radiographic Measurement Manual. Medtronic Sofamor Danek, USA (2008)Google Scholar
  15. 15.
    Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MCV 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-18421-5_11 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hongbo Wu
    • 1
  • Chris Bailey
    • 1
    • 3
  • Parham Rasoulinejad
    • 1
    • 3
  • Shuo Li
    • 1
    • 2
    Email author
  1. 1.Department of Medical ImagingWestern UniveristyLondonCanada
  2. 2.Digital Imaging Group (DIG)LondonCanada
  3. 3.London Health Sciences CenterLondonCanada

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