通过共形不变超弹性正则化学习同胚图像配准
Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
AI总结：
针对深度学习图像配准缺乏拓扑保持理论保证的问题，提出一种基于共形不变超弹性正则化和坐标MLP的可变形图像配准框架，严格保证了变形场的平滑、可逆与拓扑保持，性能优于现有技术。
摘要：
可变形图像配准是医学图像分析中的一项基础任务，并在广泛的临床应用中发挥着关键作用。近期，基于深度学习的方法在可变形医学图像配准中被广泛研究，并取得了令人鼓舞的成果。然而，现有的深度学习图像配准技术并不能在理论上保证拓扑保持的变换。这一关键属性对于保留解剖结构以及实现可用于真实临床环境的合理变换至关重要。我们提出了一种用于可变形图像配准的新颖框架。首先，我们在非线性弹性设置中引入了一种基于共形不变性质的新颖正则化器。我们的正则化器强制变形场保持平滑、可逆且方向保持。更重要的是，我们严格保证了拓扑保持，从而产生具有临床意义的配准。其次，我们通过坐标MLP提升了正则化器的性能，在这些坐标MLP中，可以将待配准图像视为连续可微的实体。我们通过数值和视觉实验证明，我们的框架能够优于当前的图像配准技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
