Deformable medical image registration: setting the state of the art with discrete methods.

By Ben Glocker, Aristeidis Sotiras, Nikos Komodakis, Nikos Paragios. 2011

218 citations recorded by the source corpus.

This review introduces a novel deformable image registration paradigm that exploits Markov random field formulation and powerful discrete optimization algorithms. We express deformable registration as a minimal cost graph problem, where nodes correspond to the deformation grid, a node's connectivity corresponds to regularization constraints, and labels correspond to 3D deformations. To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem. The two graphs share interconnected variables, leading to a modular, powerful, and flexible formulation that can account for arbitrary image-matching criteria, various local deformation models, and regularization constraints. To cope with the corresponding optimization problem, we adopt two optimization strategies: a computationally efficient one and a tight relaxation alternative. Promising results demonstrate the potential of this approach. Discrete methods are an important new trend in medical image registration, as they provide several improvements over the more traditional continuous methods. This is illustrated with several key examples where the presented framework outperforms existing general-purpose registration methods in terms of both performance and computational complexity. Our methods become of particular interest in applications where computation time is a critical issue, as in intraoperative imaging, or where the huge variation in data demands complex and application-specific matching criteria, as in large-scale multimodal population studies. The proposed registration framework, along with a graphical interface and corresponding publications, is available for download for research purposes (for Windows and Linux platforms) from http://www.mrf-registration.net.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Survey of Non-Rigid Registration Tools in Medicine (2017, methodology): “4 bUnwarpJ MSD Inverse consistent B-spline [60] 5 DRAMMS CC, SSD Deformable registration via attribute matching and mutual-saliency weighting [62] 6 Drop SAD, SADG, SSD, NCC, NMI, CR, CCGIP, HD, JRD, MI, JE, GRAD Deformable image registration based on Markov random fields and discrete optimization [”
Slice-to-volume medical image registration: a survey (2017, methodology): “Nevertheless, recent works on image registration have focused on discrete formulations (Glocker and Sotiras, 2011; Glocker et al., 2008), both for projective and sliced 2D/3D registration (Zikic et al.”
MSM: a new flexible framework for Multimodal Surface Matching☆ (2014, unknown): “More comprehensive overviews, with technical details, can be found in Glocker et al. (2008, 2011) and Wang et al. (2013).”
MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration (2012, methodology): “(2000) for rigid registration and in Haber and Modersitzki (2006), Heinrich et al.”
Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, background): “Like DROP, MSM has advantages in terms of reduced sensitivity to local minima (Glocker et al., 2011), and a modular optimisation framework.”
Deformable Medical Image Registration: A Survey (2013, methodology): “[434], [435] used FastPD to infer the displacements of a grid-based deformation model in image registration.”
Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems (2014, unknown): “A primal-dual based optimization framework has been recent ly proposed in [127], [128] for the problem of”