Abstract
We previously developed a hybrid spatio-temporal method for the segmentation of the left ventricle in 2D+time magnetic resonance (MR) image sequences and here extend this model-based approach towards 3D+time sparse stacks of cine MR images with random orientation. The presented method combines an explicit landmark based statistical geometric model of the inter-subject variability at the end-diastolic and end-systolic time frames with an implicit geometric model that constraints the intra-subject frame-to-frame temporal deformations through deterministic non-rigid image registration of adjacent frames. This hybrid model is driven by both local and global intensity similarity, resulting in a combined spatio-temporal segmentation and registration approach. The advantage of our hybrid model is that the segmentation of all image slices and of the whole sequence can be performed at once, guided by shape and intensity information of all time frames. In addition, prior shape and intensity knowledge are incorporated in order to cope with ambiguity in the images, while keeping training requirements limited.
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References
Cootes, T.F., Taylor, C.J., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Hamarneh, G., Gustavsson, T.: Deformable spatio-temporal shape models: extending active shape models to 2D+time. Image Vision Computing 22, 461–470 (2004)
Oost, C.R., Lelieveldt, B., Üzümcü, M., Lamb, H.J., Reiber, J.H.C., Sonka, M.: Multi-view Active Appearance Models: Application to X-Ray LV Angiography and Cardiac MRI. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 234–245. Springer, Heidelberg (2003)
van Assen, H.C., Danilouchkine, M.G., Frangi, A.F., Ordás, S., Westenberg, J.J.M., Reiber, J.H.C., Lelieveldt, B.P.F.: SPASM: A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Medical Image Analysis 10(2), 286–303 (2006)
Hautvast, G., Lobregt, S., Breeuwer, M., Gerritsen, F.: Automatic contour propagation in cine cardiac magnetic resonance images. IEEE Transactions on Medical Imaging 25(11), 1472–1482 (2006)
Perperidis, D., Mohiaddin, R., Edwards, P., Rueckert, D.: Segmentation of cardiac MR and CT image sequences using model based registration of a 4D statistical model. Progress in Biomedical Optics and Imaging 8(1), D5121 (2007)
Elen, A., Bogaert, J., Maes, F., Suetens, P.: A spatio-temporal model-for joint segmentation and registration of cardiac cine MR images. Submitted to Medical Image Analysis (2011)
Cremers, D.: Statistical shape knowledge in variational image segmentation. PhD thesis, Universität Mannheim (2002)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)
Kadish, A.H., Bello, D., Finn, J.P., Bonow, R.O., Schaechter, A., Subacius, H., Albert, C., Daubert, J.P., Fonseca, C.G., Goldberger, J.J.: Rationale and design for the defibrillators to reduce risk by magnetic resonance imaging evaluation (DETERMINE) trial. Journal of Cardiovascular Electrophysiology 20(9), 982–987 (2009)
Elen, A., Hermans, J., Ganame, J., Loeckx, D., Bogaert, J., Maes, F., Suetens, P.: Automatic 3-D breath-hold related motion correction of dynamic multislice MRI. IEEE Transactions on Medical Imaging 29(3), 868–878 (2010)
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Elen, A., Hermans, J., Hermans, H., Maes, F., Suetens, P. (2012). A 3D+Time Spatio-temporal Model for Joint Segmentation and Registration of Sparse Cardiac Cine MR Image Stacks. 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_20
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DOI: https://doi.org/10.1007/978-3-642-28326-0_20
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