Discriminative Context Modeling Using Auxiliary Markers for LV Landmark Detection from a Single MR Image

  • Xiaoguang Lu
  • Marie-Pierre Jolly
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7746)


Cardiac magnetic resonance imaging (MRI) is a key diagnostic tool for non-invasive assessment of the function and structure of the cardiovascular system in clinical practice. Cardiac landmarks provide strong cues to navigate the complex heart anatomy, extract and evaluate morphological and functional features for diagnosis and disease monitoring. A fully automatic method is presented to detect cardiac landmarks from individual images using a learning-based approach to model discriminative context. In addition to the target landmarks, auxiliary markers are taken into consideration to construct context with more discriminative power. The presented approach is evaluated on the STACOM2012 database, containing 100 independent test cases. Automatic landmark detection targets include two mitral valve landmarks in a long axis image, two RV insert landmarks in a short-axis image, and one central axis point in an LV base image.


Mitral Valve Cardiac Magnetic Resonance Context Modeling Short Axis Image Landmark Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Finn, J.P., Nael, K., Deshpande, V., Ratib, O., Laub, G.: Cardiac MR imaging: State of the technology. Radiology 241(2), 338–354 (2006)CrossRefGoogle Scholar
  2. 2.
    Frangi, A., Niessen, W., Viergever, M.: Three-dimensional modeling for functional analysis of cardiac images: A review. IEEE Trans. on Medical Imaging 20(1), 2–25 (2001)CrossRefGoogle Scholar
  3. 3.
    Young, A., Cowan, B., Thrupp, S., Hedley, W., Dell’Italia, L.: Left ventricular mass and volume: Fast calculation with guide-point modeling on MR images. Radiology 216(2), 597–602 (2000)Google Scholar
  4. 4.
    Jolly, M.-P., Guetter, C., Lu, X., Xue, H., Guehring, J.: Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2011. LNCS, vol. 7085, pp. 98–108. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Cerqueira, M., Weissman, N., Dilsizian, V., Jacobs, A., Kaul, S., Laskey, W., Pennell, D., Rumberger, J., Ryan, T., Verani, M.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105(4), 539–542 (2002)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  7. 7.
    Lu, X., Georgescu, B., Jolly, M.-P., Guehring, J., Young, A., Cowan, B., Littmann, A., Comaniciu, D.: Cardiac Anchoring in MRI through Context Modeling. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 383–390. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Fonseca, C., Backhaus, M., Bluemke, D., Britten, R., Chung, J., Cowan, B.R., Dinov, I., Finn, J., Hunter, P., Kadish, A., Lee, D., Lima, J., Medrano-Gracia, P., Shivkumar, K., Suinesiaputra, A., Tao, W., Young, A.: The cardiac atlas project - an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)CrossRefGoogle Scholar
  9. 9.
    Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: Proc. ICCV, pp. 1589–1596 (2005)Google Scholar
  10. 10.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features. In: Proc. ICCV (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaoguang Lu
    • 1
  • Marie-Pierre Jolly
    • 1
  1. 1.Corporate TechnologySiemens CorporationPrincetonUSA

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