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Automatic Cardiac Segmentation Using Semantic Information from Random Forests

Mahapatra, Dwarikanath

In: Journal of Digital Imaging, 2014, vol. 27, no. 6, p. 794-804

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    Titre
    • Automatic Cardiac Segmentation Using Semantic Information from Random Forests
    Auteur
    • Mahapatra, Dwarikanath. Department of Computer Science, Swiss Federal Institute of Technology, CAB E65.1, Universitatstrasse 6, 8092, Zurich, Switzerland
    Type de document
    • Postprint
    Langue
    • Inglese
    Publié dans
    • Journal of Digital Imaging, 2014, vol. 27, no. 6, p. 794-804. Springer US; http://www.springer-ny.com
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:324747
    Summary
    We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The superpixels containing RV regions constitute the region of interest (ROI) which is used to segment the actual RV. Probability maps are generated for each ROI pixel using a second set of RF classifiers which give the probabilities of each pixel belonging to RV or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low-level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that compared to conventional method our algorithm achieves superior performance due to the inclusion of semantic knowledge and context information.