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Objective: Deformable image registration is a fundamental problem in medicalimage analysis, with applications such as longitudinal studies, populationmodeling, and atlas based image segmentation. Registration is often phrased asan optimization problem, i.e., finding a deformation field that is optimalaccording to a given objective function. Discrete, combinatorial, optimizationtechniques have successfully been employed to solve the resulting optimizationproblem. Specifically, optimization based on $\alpha$-expansion with minimalgraph cuts has been proposed as a powerful tool for image registration. Thehigh computational cost of the graph-cut based optimization approach, however,limits the utility of this approach for registration of large volume images.Methods: Here, we propose to accelerate graph-cut based deformable registrationby dividing the image into overlapping sub-regions and restricting the$\alpha$-expansion moves to a single sub-region at a time. Results: Wedemonstrate empirically that this approach can achieve a large reduction incomputation time -- from days to minutes -- with only a small penalty in termsof solution quality. Conclusion: The reduction in computation time provided bythe proposed method makes graph cut based deformable registration viable forlarge volume images. Significance: Graph cut based image registration haspreviously been shown to produce excellent results, but the high computationalcost has hindered the adoption of the method for registration of large medicalvolume images. Our proposed method lifts this restriction, requiring only asmall fraction of the computational cost to produce results of comparablequality.

Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images