Abstract
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 – http://segment.heiberg.se).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- MRI :
-
Magnetic Resonance Imaging
- CT :
-
Computed Tomography
- SSFP :
-
Steady-State Free Precession
- LGE :
-
Late Gadolinium Enhancement
- EF :
-
Ejection Fraction
- SV :
-
Stroke Volume
- SA :
-
Short Axis
- LV :
-
Left Ventricle
- PCA :
-
Percutaneous Coronary Angioplasty
- MI :
-
Myocardial Infract
References
Petitjean, C., Dacher, J.-N.: A review of segmentation methods in short axis cardiac MR images. Med. Im. Anal. 15, 169–184 (2011)
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)
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)
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 2005
cmr\({}^{42}\). [software]. Circle Cardiovascular Imaging Inc., Calgary, Canada
MATLAB, v8.2 (R2013b). Natick, Massachusetts: The MathWorks Inc., 2012
Balkay, L.: DICOMDIR reader. University of Debrecen (2011). http://www.mathworks.co.uk/matlabcentral/fileexchange/7926-dicomdir-reader
Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer, Dordrecht (2000)
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Papastylianou, T., Kelly, C., Villard, B., Dall’ Armellina, E., Grau, V. (2015). Fuzzy Segmentation of the Left Ventricle in Cardiac MRI Using Physiological Constraints. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-20309-6_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20308-9
Online ISBN: 978-3-319-20309-6
eBook Packages: Computer ScienceComputer Science (R0)