Abstract
Automatic segmentation of the heart’s left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Flegel, K.M., Shipley, M.J., Rose, G.: Risk of stroke in non-rheumatic atrial fibrillation. Lancet 1, 526–529 (1987)
Cha, Y.M., Redfield, M.M., Shen, W.K., Gersh, B.J.: Atrial fibrillation and ventricular dysfunction: a vicious electromechanical cycle. Circulation 109, 2839–2843 (2004)
Reddy, V.Y., Schmidt, E.J., Holmvang, G., Fung, M.: Arrhythmia recurrence after atrial fibrillation ablation: Can magnetic resonance imaging identify gaps in atrial ablation lines? Journal of Cardiovascular Electrophysiology, 434–437 (2007)
Ecabert, O., Peters, J., Weese, J.: Modeling shape variability for full heart segmentation in cardiac computed-tomography images. In: Proc. of SPIE Medical Imaging: Image Processing (2006)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Transactions on Medical Imaging 27(11), 1668–1681 (2008)
Lorenz, C., von Berg, J.: A comprehensive shape model of the heart. Medical Image Analysis 10(4), 657–670 (2006)
John, M., Rahn, N.: Automatic left atrium segmentation by cutting the blood pool at narrowings. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 798–805. Springer, Heidelberg (2005)
Karim, R., Mohiaddin, R., Rueckert, D.: Left atrium segmentation for atrial fibrillation ablation. In: Proc. of SPIE Medical Imaging: Visualization, Image-guided Procedures, and Modeling (2008)
Karim, R., Mohiaddin, R., Rueckert, D.: Left atrium pulmonary veins: Segmentation and quantification for planning atrial fibrillation ablation. In: Proc. of SPIE Medical Imaging: Visualization, Image-guided Procedures, and Modeling (2009)
Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)
Sabuncu, M.R., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P.: Nonparametric mixture models for supervised image parcellation. In: Proc. of PMMIA 2009: MICCAI Workshop on Probabilistic Models for Medical Image Analysis, pp. 301–313 (2009)
Išgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M.A., van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion – application to cardiac and aortic segmentation in CT scans. IEEE Transactions on Medical Imaging 28(7), 1000–1009 (2009)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)
Dru, F., Vercauteren, T.: An ITK implementation of the symmetric log-domain diffeomorphic demons algorithm. Insight Journal (2009)
Wells, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Medical Image Analysis 1(1), 35–51 (1996)
Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)
Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)
Guimond, A., Roche, A., Ayache, N., Meunier, J.: Three-dimensional multimodal brain warping using the demons algorithm and adaptive intensity corrections. IEEE Transactions on Medical Imaging 20(1), 58–69 (2001)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4), 1428–1442 (2004)
Van Leemput, K., Maes, F., Vandermeulen, D., Seutens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18(10), 897–908 (1999)
Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P.: Image-driven population analysis through mixture modeling. IEEE Transactions on Medical Imaging 28(9), 1473–1487 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Depa, M., Sabuncu, M.R., Holmvang, G., Nezafat, R., Schmidt, E.J., Golland, P. (2010). Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. STACOM 2010. Lecture Notes in Computer Science, vol 6364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15835-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-15835-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15834-6
Online ISBN: 978-3-642-15835-3
eBook Packages: Computer ScienceComputer Science (R0)