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

10/19/2018
by   Simon Ekström, et al.
2

Objective: Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas based image segmentation. Registration is often phrased as an optimization problem, i.e., finding a deformation field that is optimal according to a given objective function. Discrete, combinatorial, optimization techniques have successfully been employed to solve the resulting optimization problem. Specifically, optimization based on α-expansion with minimal graph cuts has been proposed as a powerful tool for image registration. The high 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 registration by dividing the image into overlapping sub-regions and restricting the α-expansion moves to a single sub-region at a time. Results: We demonstrate empirically that this approach can achieve a large reduction in computation time -- from days to minutes -- with only a small penalty in terms of solution quality. Conclusion: The reduction in computation time provided by the proposed method makes graph cut based deformable registration viable for large volume images. Significance: Graph cut based image registration has previously been shown to produce excellent results, but the high computational cost has hindered the adoption of the method for registration of large medical volume images. Our proposed method lifts this restriction, requiring only a small fraction of the computational cost to produce results of comparable quality.

READ FULL TEXT

page 1

page 5

page 6

research
08/13/2020

CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration

Image registration is a fundamental task in medical image analysis. Rece...
research
12/09/2021

DiffuseMorph: Unsupervised Deformable Image Registration Along Continuous Trajectory Using Diffusion Models

Deformable image registration is one of the fundamental tasks for medica...
research
01/20/2020

Learning Deformable Registration of Medical Images with Anatomical Constraints

Deformable image registration is a fundamental problem in the field of m...
research
08/07/2021

Deformable Image Registration using Neural ODEs

Deformable image registration, aiming to find spatial correspondence bet...
research
03/08/2022

Region Specific Optimization (RSO)-based Deep Interactive Registration

Medical image registration is a fundamental and vital task which will af...
research
11/15/2017

Fast Predictive Simple Geodesic Regression

Deformable image registration and regression are important tasks in medi...
research
03/08/2023

MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images

Finding a realistic deformation that transforms one image into another, ...

Please sign up or login with your details

Forgot password? Click here to reset