Abstract
We propose a novel deformable model with statistical and deterministic components for LV segmentation in cardiac magnetic resonance (MR) cine images. The statistical deformable component learns a global reference model of the LV using Principal Component Analysis (PCA) while the deterministic deformable component consists of a finite-element deformable surface superimposed on the reference model. The statistical model accounts for most of the global variations in shape found in the training set while the deterministic skin accounts for the local deformations consistent with the detailed image features. Intensity gradient-based image forces are applied to the model to segment and reconstruct LV shape. We validate our model on the MICCAI Grand Challenge dataset using leave-one-out training. Comparing the automated segmentation to the manual segmentation yields a Mean Perpendicular Distance (MPD) of 3.65 mm and a Dice coefficient of 0.86.
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References
Petitjean, C., Dacher, J.: A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis 15, 169–184 (2011)
McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: A survey. Medical Image Analysis 1(2), 91–108 (1996)
McInerney, T., Terzopoulos, D.: A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to 4-D image analysis. Comp. Med. Imag. Grap. 19(1), 69–83 (1995)
Terzopoulos, D., Metaxas, D.: Dynamic 3D models with local and global deformations: Deformable superquadrics. IEEE Transactions on PAMI 13(7), 703–714 (1991)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models: Their training and application. Comp. Vis. Imag. Understanding 61, 38–59 (1995)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Mitchell, S.C., Bosch, J.G., Lelieveldt, B.P.F., van der Geest, R.J., Reiber, J.H.C., Sonka, M.: 3D Active appearance models: Segmentation of cardiac MR and ultrasound images. IEEE Transactions on Medical Imaging 21(9), 1167–1178 (2002)
Mitchell, S.C., Lelieveldt, B.P.F., van der Geest, R.J., Bosch, H.G., Reiber, J.H.C., Sonka, M.: Multistage hybrid Active Appearance Model matching: Segmentation of left and right ventricles in cardiac MR images. IEEE Transactions on Medical Imaging 20(5), 415–423 (2001)
Zhang, H., Wahle, A., Johnson, R.K., Scholz, T.D., Sonka, M.: 4-D cardiac MR image analysis: Left and right ventricular morphology and function. IEEE Transactions on Medical Imaging 29(2), 350–364 (2010)
Stegmann, M.B., Perdersen, D.: Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation. Proc. of SPIE Medical Imaging, 5747, 336–350 (2005)
Gopal, S., Otaki, Y., Arsanjani, R., Berman, D., Terzopoulos, D., Slomka, P.: Combining active appearance and deformable superquadric models for LV segmentation in cardiac MRI. Proc. SPIE Medical Imaging, 8669-15:1–8 (2013)
Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A.J., Wright, G.A.: Evaluation framework for algorithms segmenting short axis cardiac MRI. The MIDAS Journal - Cardiac MR Left Ventricle Segmentation Challenge (2009), http://hdl.handle.net/10380/3070
Kaus, M., Von Berg, J., Weese, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. Medical Image Analysis 8(3), 245–254 (2004)
Jolly, M.: Fully automatic left ventricle segmentation in cardiac cine MR images using registration and minimum surfaces. The MIDAS Journal—Cardiac MR Left Ventricle Segmentation Challenge (2009)
Huang, S., Liu, J., Lee, L., Venkatesh, S., Teo, L., Au, C., Nowinski, W.: Segmentation of the left ventricle from cine MR images using a comprehensive approach. The MIDAS Journal—Cardiac MR Left Ventricle Segmentation Challenge (2009)
Lu, Y., Radau, P., Connelly, K., Dick, A., Wright, G.: Automatic image-driven segmentation of left ventricle in cardiac cine MRI. The MIDAS Journal—Cardiac MR Left Ventricle Segmentation Challenge (2009)
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Gopal, S., Terzopoulos, D. (2014). A Unified Statistical/Deterministic Deformable Model for LV Segmentation in Cardiac MRI. 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 2013. Lecture Notes in Computer Science, vol 8330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54268-8_21
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DOI: https://doi.org/10.1007/978-3-642-54268-8_21
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