Fuzzy Segmentation of the Left Ventricle in Cardiac MRI Using Physiological Constraints

  • Tasos PapastylianouEmail author
  • Christopher Kelly
  • Benjamin Villard
  • Erica Dall’ Armellina
  • Vicente Grau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)


We describe a general framework for adapting existing segmentation algorithms, such that the need for optimisation of intrinsic, potentially unintuitive parameters is minimized, focusing instead on applying intuitive physiological constraints. This allows clinicians to easily influence existing tools of their choice towards outcomes with physiological properties that are more relevant to their particular clinical contexts, without having to deal with the optimisation specifics of a particular algorithm’s intrinsic parameters. This is achieved by a structured exploration of the parameter space resulting in a subspace of relevant segmentations, and by subsequent fusion biased towards segmentations that best adhere to the imposed constraints. We demonstrate this technique on an algorithm used by a validated, and freely available cardiac segmentation suite (Segment


cineMRI Heart Probabilistic Segmentation  



Magnetic Resonance Imaging


Computed Tomography


Steady-State Free Precession


Late Gadolinium Enhancement


Ejection Fraction


Stroke Volume


Short Axis


Left Ventricle


Percutaneous Coronary Angioplasty


Myocardial Infract



TP and BV acknowledge the support of the RCUK Digital Economy Programme grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation). VG is supported by a BBSRC grant (BB/I012117/1), an EPSRC grant (EP/J013250/1) and by BHF New Horizon Grant NH/13/30238.


  1. 1.
    Petitjean, C., Dacher, J.-N.: A review of segmentation methods in short axis cardiac MR images. Med. Im. Anal. 15, 169–184 (2011)CrossRefGoogle Scholar
  2. 2.
    Schapire, R.E.: The boosting approach to machine learning: an overview. In: Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds.) Nonlinear Estimation and Classification. LNS, pp. 149–172. Springer, New York (2003)CrossRefGoogle Scholar
  3. 3.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903–921 (2004)CrossRefGoogle Scholar
  4. 4.
    Heiberg, E., Wigstrom, L., Carlsson, M., Bolger, A. F., Karlsson, M.: Time resolved three-dimensional automated segmentation of the left ventricle. In: Computers in Cardiology, pp. 599–602. IEEE, September 2005Google Scholar
  5. 5.
    cmr\({}^{42}\). [software]. Circle Cardiovascular Imaging Inc., Calgary, CanadaGoogle Scholar
  6. 6.
    MATLAB, v8.2 (R2013b). Natick, Massachusetts: The MathWorks Inc., 2012Google Scholar
  7. 7.
    Balkay, L.: DICOMDIR reader. University of Debrecen (2011).
  8. 8.
    Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer, Dordrecht (2000)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tasos Papastylianou
    • 1
    Email author
  • Christopher Kelly
    • 1
  • Benjamin Villard
    • 1
  • Erica Dall’ Armellina
    • 2
  • Vicente Grau
    • 1
  1. 1.Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
  2. 2.Acute Vascular Imaging CentreJohn Radcliffe HospitalOxfordUK

Personalised recommendations