Abstract
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.
E. Ferrante and P.K. Dokania—Equal contribution.
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
IBSR: Internet Brain Segmentation Repository. http://www.cma.mgh.harvard.edu/ibsr/
Bronstein, M.M., Bronstein, A.M., Michel, F., Paragios, N.: Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: CVPR 2010, pp. 3594–3601. IEEE (2010)
Cifor, A., Risser, L., Chung, D., Anderson, E.M., Schnabel, J.A.: Hybrid feature-based Log-Demons registration for tumour tracking in 2-D liver ultrasound images. In: ISBI (2012)
Cifor, A., Risser, L., Chung, D., Anderson, E.M., Schnabel, J.A.: Hybrid feature-based diffeomorphic registration for tumor tracking in 2-D liver ultrasound images. IEEE TMI 32, 1647–1656 (2013)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731–741 (2008)
Joachims, T., Finley, T., Yu, C.: Cutting-plane training of structural SVMs. Mach. Learn. 77, 27–59 (2009)
Komodakis, N., Tziritas, G., Paragios, N.: Fast, approximately optimal solutions for single and dynamic MRFs. In: CVPR (2007)
Lee, D., Hofmann, M., Steinke, F., Altun, Y., Cahill, N.D., Scholkopf, B.: Learning similarity measure for multi-modal 3D image registration. In: CVPR 2009, IEEE Conference on Computer Vision and Pattern Recognition, pp. 186–193. IEEE (2009)
Michel, F., Bronstein, M., Bronstein, A., Paragios, N.: Boosted metric learning for 3D multi-modal deformable registration. In: ISBI (2011)
Rueckert, D., Sonoda, L.I., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE TMI 18, 712–721 (1999)
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., Komodakis, N.: A deep metric for multimodal registration. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 10–18. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_2
Tang, L., Hero, A., Hamarneh, G.: Locally-adaptive similarity metric for deformable medical image registration. In: ISBI. IEEE (2012)
Taskar, B., Guestrin, C., Koller, D.: Max-margin Markov networks. In: NIPS (2003)
Toga, A.W.: Learning based coarse-to-fine image registration. In: CVPR (2008)
Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML (2004)
Yu, C.N., Joachims, T.: Learning structural SVMs with latent variables. In: ICML (2009)
Yuille, A., Rangarajan, A.: The concave-convex procedure. Neural Comput. 15, 915–936 (2003)
Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ferrante, E., Dokania, P.K., Marini, R., Paragios, N. (2017). Deformable Registration Through Learning of Context-Specific Metric Aggregation. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-67389-9_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67388-2
Online ISBN: 978-3-319-67389-9
eBook Packages: Computer ScienceComputer Science (R0)