Detection and Localization of Landmarks in the Lower Extremities Using an Automatically Learned Conditional Random Field

  • Alexander Oliver Mader
  • Cristian Lorenz
  • Martin Bergtholdt
  • Jens  von  Berg
  • Hauke Schramm
  • Jan Modersitzki
  • Carsten Meyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10551)

Abstract

The detection and localization of single or multiple landmarks is a crucial task in medical imaging. It is often required as initialization for other tasks like segmentation or registration. A common approach to localize multiple landmarks is to exploit their spatial correlations, e.g., by using a conditional random field (CRF) to incorporate geometric information between landmark pairs. This CRF is usually applied to resolve ambiguities of a localizer, e.g., a random forest or a deep neural network. In this paper, we apply a random forest/CRF combination to the task of jointly detecting and localizing 6 landmarks in the lower extremities, taken from a dataset of 660 X-ray images. The dataset is challenging since a significant number of images does not show all the landmarks. Furthermore, 11.3% of the target landmarks are altered by prostheses or pathologies.

To account for this, we introduce a “missing” label for each landmark (represented by a node in the CRF). Moreover, instead of manually specifying the CRF model by selecting suitable potential functions and the graph topology, we suggest to automatically optimize both in a learning framework. Specifically, we define a pool of potential functions and learn their CRF weights (relative contributions), in addition to the potential values in case of missing landmarks. Potentials with a low weight are removed, thus optimizing the graph topology. Detailed evaluations on our database show the feasibility of our approach. Our algorithm removed on average 23 of the initial 51 CRF potentials, and correctly detected and localized (within 10 mm tolerance) on average 92.8% of the landmarks, with individual rates ranging from 90.0% to 97.4%.

Notes

Acknowledgements

The authors thank the Diagnosezentrum Urania, Vienna and the Dartmouth Hitchcock Medical Center, Lebanon for providing the radiographs that served as training and test sets; Gooßen [8] for the annotations. This work has been financially supported by the Federal Ministry of Education and Research under the grant 03FH013IX5. The liability for the content of this work lies with the authors.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alexander Oliver Mader
    • 1
    • 2
    • 3
  • Cristian Lorenz
    • 3
  • Martin Bergtholdt
    • 3
  • Jens  von  Berg
    • 3
  • Hauke Schramm
    • 1
    • 2
  • Jan Modersitzki
    • 4
  • Carsten Meyer
    • 1
    • 2
    • 3
  1. 1.Institute of Computer ScienceKiel University of Applied SciencesKielGermany
  2. 2.Department of Computer Science, Faculty of EngineeringKiel UniversityKielGermany
  3. 3.Department of Digital ImagingPhilips ResearchHamburgGermany
  4. 4.Institute of Mathematics and Image ComputingLübeck UniversityLübeckGermany

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