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Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Mahapatra, Dwarikanath

In: Journal of Digital Imaging, 2013, vol. 26, no. 5, p. 898-908

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    Title
    • Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle
    Author
    • Mahapatra, Dwarikanath. Department of Computer Science, Swiss Federal Institute of Technology (ETH) Zurich, Room CAB F 61.1 Universitätstrasse, 68092, Zurich, Switzerland
    Document Type
    • Postprint
    Language
    • English
    Published in
    • Journal of Digital Imaging, 2013, vol. 26, no. 5, p. 898-908. Springer US; http://www.springer-ny.com
    Other Version
    OAI-PMH Identifier
    • oai:doc.rero.ch:317390
    Summary
    In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty” that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentations