Abstract
Segmentation of the left atria (LA) from late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is challenging since atrial borders are not easily distinguishable in the images. We propose a method based on multi-atlas whole heart segmentation and shape modeling of the LA. In the training phase we first construct whole heart LGE-MRI atlases and build a principal component analysis (PCA) model able to capture the high variability of the LA shapes. All atlases are clustered according to their LA shape using an unsupervised clustering method which additionally outputs the most representative case in each cluster. All cluster representatives are registered to the target image and ranked using conditional entropy. A small subset of the most similar representatives is used to find LA shapes with similar morphology in the training set that are used to obtain the final LA segmentation. We tested our approach using 80 LGE-MRI data for training and 20 LGE-MRI data for testing obtaining a Dice score of \(0.842 \pm 0.049\).
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
References
Andrade, J., Khairy, P., Dobrev, D., Nattel, S.: The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. Circ. Res. 114(9), 1453–1468 (2014)
Chen, J., et al.: Multiview two-task recursive attention model for left atrium and atrial scars segmentation. arXiv preprint arXiv:1806.04597 (2018)
Depa, M., Sabuncu, M.R., Holmvang, G., Nezafat, R., Schmidt, E.J., Golland, P.: 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.) STACOM 2010. LNCS, vol. 6364, pp. 85–94. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15835-3_9
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
GarcĂa-Isla, G., et al.: Sensitivity analysis of geometrical parameters to study haemodynamics and thrombus formation in the left atrial appendage. Int. J. Numer. Meth. Biomed. Eng. 34, e3100 (2018)
Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24(1), 205–219 (2015)
Langerak, T.R., Berendsen, F.F., Van der Heide, U.A., Kotte, A.N., Pluim, J.P.: Multiatlas-based segmentation with preregistration atlas selection. Med. Phys. 40(9), 091701 (2013)
Prasanna, L., Praveena, R., D’Souza, A.S., Kumar, M.: Variations in the pulmonary venous ostium in the left atrium and its clinical importance. J. Clin. Diagn. Res. 8(2), 10 (2014)
Rueckert, D., Lorenzo-Valdes, M., Chandrashekara, R., Sanchez-Ortiz, G., Mohiaddin, R.: Non-rigid registration of cardiac MR: application to motion modelling and atlas-based segmentation. In: Proceedings of 2002 IEEE International Symposium on Biomedical Imaging, pp. 481–484 (2002)
Sanroma, G., Wu, G., Gao, Y., Shen, D.: Learning-based atlas selection for multiple-atlas segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3111–3117 (2014)
Tao, Q., Ipek, E.G., Shahzad, R., Berendsen, F.F., Nazarian, S., van der Geest, R.J.: Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: towards objective atrial scar assessment. J. Magn. Reson. Imaging 44(2), 346–354 (2016)
Tobon-Gomez, C., et al.: Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Trans. Med. Imaging 34(7), 1460–1473 (2015)
Vos, T., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet 388(10053), 1545–1602 (2016)
Zhuang, X., et al.: Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Med. Phys. 42(7), 3822–3833 (2015)
Zhuang, X., Rhode, K.S., Razavi, R.S., Hawkes, D.J., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imaging 29(9), 1612–1625 (2010)
Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77–87 (2016)
Acknowledgments
This study was partially funded by the Spanish Ministry of Economy and Competitiveness (DPI2015-71640-R) and by the “Fundació La Marató de TV3” (no 20154031).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nuñez-Garcia, M. et al. (2019). Left Atrial Segmentation Combining Multi-atlas Whole Heart Labeling and Shape-Based Atlas Selection. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-12029-0_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12028-3
Online ISBN: 978-3-030-12029-0
eBook Packages: Computer ScienceComputer Science (R0)