A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation

Authors:
Wenzhe Shi;Xiahai Zhuang;Robin Wolz;Duckett Simon;KaiPin Tung;Haiyan Wang;Sebastien Ourselin;Philip Edwards;Reza Razavi;Daniel Rueckert
Affiliations:
Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK;Centre for Medical Image Computing, Department of Computing, University College London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK;The Rayne Institute, Kings College London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK;Centre for Medical Image Computing, Department of Computing, University College London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK;The Rayne Institute, Kings College London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
Venue:
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Year:
2011

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Abstract

Registration and segmentation uncertainty may be important information to convey to a user when automatic image analysis is performed. Uncertainty information may be used to provide additional diagnostic information to traditional analysis of cardiac function. In this paper, we develop a framework for the automatic segmentation of the cardiac anatomy from multiple MR images. We also define the registration and segmentation uncertainty and explore its use for diagnostic purposes. Our framework uses cardiac MR image sequences that are widely available in clinical practice. We improve the performance of the cardiac segmentation algorithms by combining information from multiple MR images using a graph-cut based segmentation. We evaluate this framework on images from 32 subjects: 13 patients with ischemic cardiomyopathy, 14 patients with dilated cardiomyopathy and 5 normal volunteers. Our results indicate that the proposed method is capable of producing segmentation results with very high robustness and high accuracy with minimal user interaction across all subject groups. We also show that registration and segmentation uncertainties are good indicators for segmentation failures as well as good predictors for the functional abnormality of the subject.