Department of Surgical Sciences, Uppsala University, Sweden.
Department of Surgical Sciences, Uppsala University, Sweden; Department of Information Technology, Uppsala University, Sweden.
Comput Med Imaging Graph. 2020 Sep;84:101745. doi: 10.1016/j.compmedimag.2020.101745. Epub 2020 Jun 19.
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. 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. 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. The reduction in computation time provided by the proposed method makes graph-cut based deformable registration viable for large volume images. 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.
变形图像配准是医学图像分析中的一个基本问题，其应用包括纵向研究、群体建模和基于图谱的图像分割。配准通常被表述为一个优化问题，即找到一个变形场，使其根据给定的目标函数达到最优。离散的、组合的优化技术已经成功地被用于解决由此产生的优化问题。具体来说，基于α扩展和最小图割的优化已经被提出作为图像配准的一种强大工具。然而，基于图割的优化方法的高计算成本限制了这种方法在大体积图像配准中的应用。在这里，我们提出通过将图像划分为重叠的子区域，并将α扩展移动限制在单个子区域内，来加速基于图割的变形配准。我们通过实验证明，这种方法可以大大减少计算时间——从几天到几分钟——而在解决方案质量方面只有很小的代价。所提出的方法提供的计算时间减少使得基于图割的变形配准适用于大体积图像。基于图割的图像配准先前已经被证明可以产生优异的结果，但高计算成本阻碍了该方法在大型医学体积图像配准中的应用。我们提出的方法消除了这一限制，只需要一小部分的计算成本就能产生质量相当的结果。
