Abstract
Patient metadata such as demographic information and cardio vascular disease (CVD) indicators are valuable data readily available in clinical practice. This information can be used to inform the construction of customized statistical shape models fitting the patient’s unique characteristics. However, to the best of our knowledge, no studies have reported using these types of metadata in the construction of shape models for image segmentation. In this paper, we propose the use of a conditional model framework to include these patient metadata in the construction of a personalized shape model and evaluate its effect on image segmentation. Our validation on a dataset of 250 asymptomatic cardiac MR images shows an average segmentation improvement of 7 % and in some cases up to 30 % over a conventional PCA-based framework. These results show the potential of our technique for improved shape analysis.
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 subscriptionsReferences
Bild, D.E., Bluemke, D.A., Burke, G.L., Detrano, R., Roux, A.V.D., Folsom, A.R., Greenland, P., Jacobs Jr., D.R., Kronmal, R., Liu, K., et al.: Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156(9), 871–881 (2002)
Blanc, R., Reyes, M., Seiler, C., Székely, G.: Conditional variability of statistical shape models based on surrogate variables. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 84–91. Springer, Heidelberg (2009)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Understand. 61(1), 38–59 (1995)
Fonseca, C.G., Backhaus, M., Bluemke, D.A., Britten, R.D., Do Chung, J., Cowan, B.R., Dinov, I.D., Finn, J.P., Hunter, P.J., Kadish, A.H., et al.: The cardiac atlas projectan imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)
Goodall, C.: Procrustes methods in the statistical analysis of shape. J. Roy. Stat. Soc. B Stat. Meth. 53, 285–339 (1991)
Grbić, S., Swee, J.K.Y., Ionasec, R.: ShapeForest: building constrained statistical shape models with decision trees. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part III. LNCS, vol. 8691, pp. 597–612. Springer, Heidelberg (2014)
Medrano-Gracia, P., Cowan, B.R., Ambale-Venkatesh, B., Bluemke, D.A., Eng, J., Finn, J.P., Fonseca, C.G., Lima, J.A., Suinesiaputra, A., Young, A.A.: Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis. Cardiovasc. Res. 16(1), 56 (2014)
Pereanez, M., Lekadir, K., Castro-Mateos, I., Pozo, J., Lazary, A., Frangi, A.: Accurate segmentation of vertebral bodies and processes using statistical shape decomposition and conditional models. IEEE Trans. Med. Imaging 34(8), 1627–1639 (2015)
Van Assen, H.C., Danilouchkine, M.G., Frangi, A.F., Ordás, S., Westenberg, J.J., Reiber, J.H., Lelieveldt, B.P.: Spasm: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med. Image Anal. 10(2), 286–303 (2006)
Wolz, R., Aljabar, P., Hajnal, J.V., Lötjönen, J., Rueckert, D., Initiative, A.D.N., et al.: Nonlinear dimensionality reduction combining MR imaging with non-imaging information. Med. Image Anal. 16(4), 819–830 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Pereañez, M., Lekadir, K., Albà, X., Medrano-Gracia, P., Young, A.A., Frangi, A. (2016). Patient Metadata-Constrained Shape Models for Cardiac Image Segmentation. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-28712-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28711-9
Online ISBN: 978-3-319-28712-6
eBook Packages: Computer ScienceComputer Science (R0)