Abstract
Registration and segmentation of anatomical structures are two well studied problems in medical imaging. Optimizing segmentation and registration jointly has been proven to improve results for both challenges. In this work, we propose a joint optimization scheme for registration and segmentation using dictionary learning based descriptors. Our joint registration and segmentation aims to solve an optimization function, which enables better performance for both of these ill-posed processes. We build two dictionaries for background and myocardium for square patches extracted from training images. Based on dictionary learning residuals and sparse representations defined on these pre-trained dictionaries, a Markov Random Field (MRF) based joint optimization scheme is built. The algorithm proceeds iteratively updating the dictionaries in an online fashion. The accuracy of the proposed method is illustrated on Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI and standard cine Cardiac MRI data from MICCAI 2013 SATA Segmentation Challenge. The proposed joint segmentation and registration method achieves higher dice accuracy for myocardium segmentation compared to its variants.
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
Similar content being viewed by others
References
Aharon, M., et al.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE TSP 54(11), 4311–4322 (2006)
Alchatzidis, A., et al.: A discrete MRF framework for integrated multi-atlas registration and segmentation. IJCV 121(1), 169–181 (2017)
Alchatzidis, S., et al.: Discrete multi atlas segmentation using agreement constraints. In: BMVC (2014)
Bevilacqua, M., et al.: Dictionary-driven ischemia detection from cardiac phase-resolved myocardial BOLD MRI at rest. IEEE TMI 35(1), 282–293 (2016)
Boykov, Y., et al.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23(11), 1222–1239 (2001)
Fonseca, C.G., et al.: The cardiac atlas projectan imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)
Gass, T., et al.: Simultaneous segmentation and multiresolution nonrigid atlas registration. IEEE TIP 23(7), 2931–2943 (2014)
Glocker, B., et al.: Dense image registration through MRFs and efficient linear programming. MedIA 12(6), 731–741 (2008)
Huang, X., et al.: Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. MedIA 18, 253–271 (2014)
Mahapatra, D., Sun, Y.: Joint registration and segmentation of dynamic cardiac perfusion images using MRFs. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 493–501. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15705-9_60
Mahapatra, D., et al.: Integrating segmentation information for improved MRF-based elastic image registration. IEEE TIP 20(1), 170–183 (2012)
Mairal, J., et al.: Online dictionary learning for sparse coding. In: ICML, pp. 689–696 (2009)
Mukhopadhyay, A., Oksuz, I., Bevilacqua, M., Dharmakumar, R., Tsaftaris, S.A.: Data-driven feature learning for myocardial segmentation of CP-BOLD MRI. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds.) FIMH 2015. LNCS, vol. 9126, pp. 189–197. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20309-6_22
Oksuz, I., Mukhopadhyay, A., Bevilacqua, M., Dharmakumar, R., Tsaftaris, S.A.: Dictionary learning based image descriptor for myocardial registration of CP-BOLD MR. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 205–213. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24571-3_25
Oksuz, I., et al.: Unsupervised myocardial segmentation for cardiac BOLD. IEEE TMI 36, 2228–2238 (2017)
Parisot, S., et al.: Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs. MedIA 18(4), 647–659 (2014)
Queiros, S., et al.: Fast automatic myocardial segmentation in 4D cine CMR datasets. MedIA 18(7), 1115–1131 (2014)
Tropp, J.A., et al.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)
Tsaftaris, S.A., et al.: Detecting myocardial ischemia at rest with cardiac phase-resolved blood oxygen level-dependent cardiovascular magnetic resonance. Circ. Cardiovasc. Imaging 6(2), 311–319 (2013)
Wang, F., et al.: Joint registration and segmentation of neuroanatomic structures from brain MRI. Acad. Radiol. 13(9), 1031–1044 (2006)
Wyatt, P.P., et al.: MAP MRF joint segmentation and registration of medical images. MedIA 7(4), 539–552 (2003)
Yezzi, A., et al.: A variational framework for integrating segmentation and registration through active contours. MedIA 7(2), 171–185 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Oksuz, I., Dharmakumar, R., Tsaftaris, S.A. (2018). Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-75541-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75540-3
Online ISBN: 978-3-319-75541-0
eBook Packages: Computer ScienceComputer Science (R0)