Abstract
In this paper we present a new method for fully automatic left ventricle segmentation from 4D cardiac MR datasets. To deal with the diverse dataset, we propose a machine learning approach using two layers of spatio-temporal decision forests with almost no assumptions on the data nor explicitly specifying the segmentation rules. We introduce 4D spatio-temporal features to classification with decision forests and propose a method for context aware MR intensity standardization and image alignment. The second layer is then used for the final image segmentation. We present our first results on the STACOM LV Segmentation Challenge 2011 validation datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lempitsky, V., Verhoek, M., Noble, J., Blake, A.: Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 447–456. Springer, Heidelberg (2009)
Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2), 378–390 (2011)
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., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 163–170. Springer, Heidelberg (2011)
Lu, X., Wang, Y., Georgescu, B., Littman, A., Comaniciu, D.: Automatic Delineation of Left and Right Ventricles in Cardiac MRI Sequences Using a Joint Ventricular Model. In: Metaxas, D., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 250–258. Springer, Heidelberg (2011)
Fonseca, C., Backhaus, M., Bluemke, D., Britten, R., Chung, J., Cowan, B., Dinov, I., Finn, J., Hunter, P., Kadish, A., Lee, D., Lima, J., Medrano-Gracia, P., Shivkumar, K., Suinesiaputra, A., Tao, W., Young, A.: The Cardiac Atlas Project- an Imaging Database for Computational Modeling and Statistical Atlases of the Heart. Bioinformatics 27(16), 2288–2295 (2011)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, pp. I-511–I-518. IEEE Computer Society (2001)
Nyúl, L.G., Udupa, J.K.: On standardizing the MR image intensity scale. Magnetic Resonance in Medicine 42(6), 1072–1081 (1999)
Shah, M., Xiao, Y., Subbanna, N., Francis, S., Arnold, D.L., Collins, D.L., Arbel, T.: Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Medical Image Analysis 15(2), 267–282 (2010)
Bergeest, J., Florian Jäger, F.: A Comparison of Five Methods for Signal Intensity Standardization in MRI. In: Tolxdorff, T., Braun, J., Deserno, T.M., Horsch, A., Handels, H., Meinzer, H.P., Brauer, W. (eds.) Bildverarbeitung für die Medizin 2008, pp. 36–40. Springer, Heidelberg (2008)
Nyúl, L.G., Udupa, J.K., Zhang, X.: New Variants of a Method of MRI Scale Standardization. IEEE Transactions on Medical Imaging 19(2), 143–150 (2000)
Jain, R., Chlamtac, I.: The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations. Communications of the ACM 28(10), 1076–1085 (1985)
Egloff, D.: Weighted P2 quantile, Boost Accumulators 1.46 (2005), www.boost.org
Konukoglu, E., Criminisi, A., Pathak, S., Robertson, D., White, S., Haynor, D., Siddiqui, K.: Robust linear registration of CT images using random regression forests. In: SPIE Medical Imaging, vol. 7962, pp. 79621X–79621X-8 (2011)
Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images. In: Delp, S., DiGoia, A., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 557–566. Springer, Heidelberg (2000)
Hoogendoorn, C., Whitmarsh, T., Duchateau, N., Sukno, F.M., De Craene, M., Frangi, A.F.: A groupwise mutual information metric for cost efficient selection of a suitable reference in cardiac computational atlas construction. In: SPIE Medical Imaging, vol. 7962, pp. 76231R–76231R-9 (2010)
Iglesias, J., Konukoglu, E., Montillo, A., Tu, Z., Criminisi, A.: Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active Learning. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 25–36. Springer, Heidelberg (2011)
Krishnan, K., Ibanez, L., Turner, W., Avila, R.: Algorithms, architecture, validation of an open source toolkit for segmenting CT lung lesions. In: Brown, M., de Bruijne, M., van Ginneken, B., Kiraly, A., Kuhnigk, J.M., Lorenz, C., McClelland, J.R., Mori, K., Reeves, A., Reinhardt, J.M. (eds.) MICCAI Workshop on Pulmonary Image Analysis, CreateSpace, pp. 365–375 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Margeta, J., Geremia, E., Criminisi, A., Ayache, N. (2012). Layered Spatio-temporal Forests for Left Ventricle Segmentation from 4D Cardiac MRI Data. 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_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-28326-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28325-3
Online ISBN: 978-3-642-28326-0
eBook Packages: Computer ScienceComputer Science (R0)