Integrating computer vision and non-linear optimization for automated deformable registration of 3D medical images

Abstract

Deformable image registration (DIR), the process of estimating and applying non-linear transforms to spatially align sets of two or more images, is a challenging task with many important clinical applications including kinetic analysis, cancer treatment targeting, and evaluation of treatment response. Current techniques use non-linear optimizations only to reach a local minimum and not a globally optimal solution, limiting application to cases with small spatial displacements. Various semi-dense feature-based methods drawing inspiration from mammalian systems as the basis for 2D visual processing have been implemented for automated wide baseline registration and object detection applications with great success. Extension to the 3D case, which has been shown to enable highly efficient coarse global image search, in theory could be adapted also to allow precise semi-dense global optimization. The algorithm described in this paper, dubbed constrained robust affine feature transform (CRAFT), incorporates paradigms from various computer-vision techniques to combine aspects of the human visual pathway with proven non-linear optimization methods to automate general deformable registration with unprecedented robustness. This hybrid technique is able to estimate registration confidence and can serve as the basis of machine perception of medical images for machine learning.