Abstract
Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MR is capable of diagnosing an ongoing ischemia by detecting changes in myocardial intensity patterns at rest without any contrast and stress agents. Visualizing and detecting these changes require significant post-processing, including myocardial segmentation for isolating the myocardium. But, changes in myocardial intensity pattern and myocardial shape due to the heart’s motion challenge automated standard CINE MR myocardial segmentation techniques resulting in a significant drop of segmentation accuracy. We hypothesize that the main reason behind this phenomenon is the lack of discernible features. In this paper, a multi scale discriminative dictionary learning approach is proposed for supervised learning and sparse representation of the myocardium, to improve the myocardial feature selection. The technique is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canine subjects. The proposed method significantly outperforms standard cardiac segmentation techniques, including segmentation via registration, level sets and supervised methods for myocardial segmentation.
The first two authors contributed equally to this work.
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Aharon, M., et al.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE TSP 54(11), 4311–4322 (2006)
Bai, W., et al.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE TMI 32(7), 1302–1315 (2013)
Chan, T.F., et al.: Active contours without edges. IEEE TIP 10(2), 266–277 (2001)
Chang, L.-H., et al.: Achievable angles between two compressed sparse vectors under norm/distance constraints imposed by the restricted isometry property: a plane geometry approach. IEEE T Inf. Theory 59(4), 2059–2081 (2013)
Glocker, B., et al.: Dense image registration through MRFs and efficient linear programming. MIA 12(6), 731–741 (2008)
Huang, X., et al.: Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. MIA 18, 253–271 (2014)
Li, C., et al.: Distance regularized level set evolution and its application to image segmentation. IEEE TIP 19(12), 3243–3254 (2010)
Ramirez, I., et al.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: IEEE CVPR, pp. 3501–3508 (2010)
Rusu, C., et al.: Synthetic generation of myocardial bloodoxygen-level-dependent MRI time series via structural sparse decomposition modeling. IEEE TMI 7(33), 1422–1433 (2014)
Tavakoli, V., et al.: A survey of shape-based registration and segmentation techniques for cardiac images. CVIU 117, 966–989 (2013)
Tong, T., et al.: Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. NeuroImage 76, 11–23 (2013)
Tropp, J.A., et al.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE T Inf. Theory 53(12), 4655–4666 (2007)
Tsaftaris, S.A., et al.: A dynamic programming solution to tracking and elastically matching left ventricular walls in cardiac CINE MRI. In: IEEE ICIP, pp. 2980–2983 (2008)
Tsaftaris, S.A., et al.: Detecting myocardial ischemia at rest with cardiac phaseresolved blood oxygen leveldependent cardiovascular magnetic resonance. Circ.: Cardiovasc. Imaging 6(2), 311–319 (2013)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)
Wright, J., et al.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)
Zhen, X., Wang, Z., Islam, A., Bhaduri, M., Chan, I., Li, S.: Direct estimation of cardiac bi-ventricular volumes with regression forests. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 586–593. Springer, Heidelberg (2014)
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This work was supported by the National Institutes of Health under Grant 2R01HL091989-05.
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Mukhopadhyay, A., Oksuz, I., Bevilacqua, M., Dharmakumar, R., Tsaftaris, S.A. (2015). Data-Driven Feature Learning for Myocardial Segmentation of CP-BOLD MRI. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_22
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DOI: https://doi.org/10.1007/978-3-319-20309-6_22
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