Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
翻译:图像登记是医学图像分析的关键技术,用于估计图像配对之间的变形。良好的变形模型对于高质量的估计很重要。但是,大多数现有方法都使用为数学便利而不是为捕捉观察到的数据变异而选择的异常变形模型。最近深层次的学习方法直接从数据中学习变形模型。但是,它们对变异的空间规律性控制有限。我们不是学习整个登记方法,而是在登记模型中学习空间适应性正规化器。这样就可以控制一个登记模型的常规性理想水平并保存其结构特性。例如,可以实现二变形变形。我们的方法根本偏离了现有的图像登记深层学习方法,在基于优化的登记算法中嵌入一个深层次的学习模型,以对登记模型本身进行参数化和数据适应。