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Automatically Localizing a Large Set of Spatially Correlated Key Points: A Case Study in Spine Imaging

  • Alexander Oliver MaderEmail author
  • Cristian Lorenz
  • Jens von Berg
  • Carsten Meyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

The fully automatic localization of key points in medical images is an important and active area in applied machine learning, with very large sets of key points still being an open problem. To this end, we extend two general state-of-the-art localization approaches to operate on large amounts of key points and evaluate both approaches on a CT spine data set featuring 102 key points. First, we adapt the multi-stage convolutional pose machines neural network architecture to 3D image data with some architectural changes to cope with the large amount of data and key points. Imprecise localizations caused by the inherent downsampling of the network are countered by quadratic interpolation. Second, we extend a common approach—regression tree ensembles spatially regularized by a conditional random field—by a latent scaling variable to explicitly model spinal size variability. Both approaches are evaluated in detail in a 5-fold cross-validation setup in terms of localization accuracy and test time on 157 spine CT images. The best configuration achieves a mean localization error of 4.21 mm over all 102 key points.

Keywords

Key point localization Fully convolutional neural network Conditional random field Spine Computed tomography 

Supplementary material

490281_1_En_43_MOESM1_ESM.pdf (3.6 mb)
Supplementary material 1 (pdf 3636 KB)

References

  1. 1.
    Bier, B., et al.: X-ray-transform invariant anatomical landmark detection for pelvic trauma surgery. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 55–63. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00937-3_7CrossRefGoogle Scholar
  2. 2.
    Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer, London (2013).  https://doi.org/10.1007/978-1-4471-4929-3Google Scholar
  3. 3.
    Donner, R., Micusik, B., Langs, G., Bischof, H.: Sparse MRF appearance models for fast anatomical structure localisation. In: BMVC, pp. 109.1–109.10 (2007)Google Scholar
  4. 4.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014)Google Scholar
  5. 5.
    Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. Med. Image Anal. 13(3), 471–482 (2009)CrossRefGoogle Scholar
  6. 6.
    Liao, H., Mesfin, A., Luo, J.: Joint vertebrae identification and localization in spinal CT images by combining short-and long-range contextual information. IEEE TMI 37(5), 1266–1275 (2018)Google Scholar
  7. 7.
    Mader, A.O., et al.: Detection and localization of spatially correlated point landmarks in medical images using an automatically learned conditional random field. CVIU 176, 45–53 (2018)Google Scholar
  8. 8.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565–571. IEEE (2016)Google Scholar
  9. 9.
    Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_27CrossRefGoogle Scholar
  10. 10.
    Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: CVPR, pp. 4724–4732 (2016)Google Scholar
  11. 11.
    Zhang, J., Liu, M., Shen, D.: Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE TIP 26(10), 4753–4764 (2017)MathSciNetGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander Oliver Mader
    • 1
    • 2
    • 3
    Email author
  • Cristian Lorenz
    • 3
  • Jens von Berg
    • 3
  • Carsten Meyer
    • 1
    • 2
    • 3
  1. 1.Institute of Computer ScienceKiel University of Applied SciencesKielGermany
  2. 2.Faculty of Engineering, Department of Computer ScienceKiel UniversityKielGermany
  3. 3.Department of Digital ImagingPhilips ResearchHamburgGermany

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