Deformable image registration is a fundamental problem in the field of medical image analysis. During the last years, we have witnessed the advent of deep learning-based image registration methods which achieve state-of-the-art performance, and drastically reduce the required computational time. However, little work has been done regarding how can we encourage our models to produce not only accurate, but also anatomically plausible results, which is still an open question in the field. In this work, we argue that incorporating anatomical priors in the form of global constraints into the learning process of these models, will further improve their performance and boost the realism of the warped images after registration. We learn global non-linear representations of image anatomy using segmentation masks, and employ them to constraint the registration process. The proposed AC-RegNet architecture is evaluated in the context of chest X-ray image registration using three different datasets, where the high anatomical variability makes the task extremely challenging. Our experiments show that the proposed anatomically constrained registration model produces more realistic and accurate results than state-of-the-art methods, demonstrating the potential of this approach.

中文翻译：

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学习具有解剖学约束的医学图像的可变形配准。

可变形图像配准是医学图像分析领域中的基本问题。在过去的几年中，我们见证了基于深度学习的图像配准方法的出现，该方法实现了最先进的性能，并大大减少了所需的计算时间。但是，关于如何鼓励我们的模型不仅产生准确的结果，而且在解剖学上似乎可行的结果，还没有完成任何工作，这在该领域仍然是一个未解决的问题。在这项工作中，我们认为将解剖先验以全局约束的形式并入这些模型的学习过程中，将进一步提高其性能并提高配准后扭曲图像的真实性。我们使用分割蒙版学习图像解剖结构的全局非线性表示，并雇用他们来限制注册过程。拟议的AC-RegNet体系结构是使用三个不同的数据集在胸部X射线图像配准的情况下进行评估的，其中较高的解剖变异性使这项任务极具挑战性。我们的实验表明，所提出的解剖学约束配准模型比最先进的方法产生更真实，更准确的结果，证明了这种方法的潜力。