Deformable image registration is a fundamental task in medical image analysis and plays a crucial role in a wide range of clinical applications. Recently, deep learning-based approaches have been widely studied for deformable medical image registration and achieved promising results. However, existing deep learning image registration techniques do not theoretically guarantee topology-preserving transformations. This is a key property to preserve anatomical structures and achieve plausible transformations that can be used in real clinical settings. We propose a novel framework for deformable image registration. Firstly, we introduce a novel regulariser based on conformal-invariant properties in a nonlinear elasticity setting. Our regulariser enforces the deformation field to be smooth, invertible and orientation-preserving. More importantly, we strictly guarantee topology preservation yielding to a clinical meaningful registration. Secondly, we boost the performance of our regulariser through coordinate MLPs, where one can view the to-be-registered images as continuously differentiable entities. We demonstrate, through numerical and visual experiments, that our framework is able to outperform current techniques for image registration.
翻译:变形图像登记是医学图像分析的一项基本任务,在广泛的临床应用中发挥着关键作用。最近,对基于深层次学习的方法进行了广泛研究,以进行变形医学图像登记,并取得了有希望的成果。然而,现有的深层次学习图像登记技术在理论上并不能保证地形保存变化。这是保存解剖结构和实现可应用于真正的临床环境的貌似转变的关键财产。我们提出了一个变形图像登记的新框架。首先,我们在非线性弹性设置中引入了基于同异特性的新型定型软件。我们的定型软件强制实施变形场要平滑、不可逆和方向保留。更重要的是,我们严格保证表层保存能够产生临床有意义的登记。第二,我们通过协调 MLPs 来提升我们的定型计算机的性能,在那里,人们可以将注册的图像视为持续不同的实体。我们通过数字和视觉实验来证明我们的框架能够超越当前图像登记技术。</s>