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.
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References
Uribe, S., Muthurangu, V., et al.: Whole-heart cine MRI using real-time respiratory self-gating. Magnetic Resonance in Medicine 57(3), 606–613 (2007)
Lorenzo-Valdés, M., Sanchez-Ortiz, G., Rueckert, D., et al.: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Medical Image Analysis 8(3), 255–265 (2004)
Zhuang, X., Rhode, K., Ourselin, S., et al.: A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI. IEEE Transactions on Medical Imaging, 1612–1625 (2010)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1222–1239 (2001)
Risholm, P., Pieper, S., Samset, E., Wells III, W.M.: Summarizing and Visualizing Uncertainty in Non-rigid Registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 554–561. Springer, Heidelberg (2010)
Kybic, J.: Bootstrap resampling for image registration uncertainty estimation without ground truth. IEEE Transactions on Image Processing 19(1), 64–73 (2009)
Shi, W., Zhuang, X., Wang, H., Duckett, S., Oregan, D., Edwards, P., Ourselin, S., Rueckert, D.: Automatic Segmentation of Different Pathologies from Cardiac Cine MRI Using Registration and Multiple Component EM Estimation. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 163–170. Springer, Heidelberg (2011)
Chandler, A., Razavi, R., et al.: Correction of misaligned slices in multi-slice MR cardiac examinations by using slice-to-volume registration. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 474–477. IEEE (2006)
Linguraru, M.G., Pura, J.A., Chowdhury, A.S., Summers, R.M.: Multi-organ Segmentation from Multi-phase Abdominal CT via 4D Graphs Using Enhancement, Shape and Location Optimization. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 89–96. Springer, Heidelberg (2010)
Wolz, R., Heckemann, R., Aljabar, P., et al.: Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI. NeuroImage (2010)
Rohlfing, T., Maurer Jr., C., Bluemke, D., Jacobs, M.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Transactions on Medical Imaging 22(6), 730–741 (2003)
Koay, C., Basser, P.: Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. Journal of Magnetic Resonance 179(2), 317–322 (2006)
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Shi, W. et al. (2012). A Multi-image Graph Cut Approach for Cardiac Image Segmentation and Uncertainty Estimation. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2011. Lecture Notes in Computer Science, vol 7085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28326-0_18
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DOI: https://doi.org/10.1007/978-3-642-28326-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28325-3
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