Diffeomorphic image registration is crucial for various medical imaging applications because it can preserve the topology of the transformation. This study introduces DCCNN-LSTM-Reg, a learning framework that evolves dynamically and learns a symmetrical registration path by satisfying a specified control increment system. This framework aims to obtain symmetric diffeomorphic deformations between moving and fixed images. To achieve this, we combine deep learning networks with diffeomorphic mathematical mechanisms to create a continuous and dynamic registration architecture, which consists of multiple Symmetric Registration (SR) modules cascaded on five different scales. Specifically, our method first uses two U-nets with shared parameters to extract multiscale feature pyramids from the images. We then develop an SR-module comprising a sequential CNN-LSTM architecture to progressively correct the forward and reverse multiscale deformation fields using control increment learning and the homotopy continuation technique. Through extensive experiments on three 3D registration tasks, we demonstrate that our method outperforms existing approaches in both quantitative and qualitative evaluations.

翻译：微分同胚图像配准对于各种医学成像应用至关重要，因为它能够保持变换的拓扑结构。本研究提出了DCCNN-LSTM-Reg，这是一种动态演化并通过满足指定的控制增量系统来学习对称配准路径的学习框架。该框架旨在获取移动图像与固定图像之间的对称微分同胚形变。为实现这一目标，我们将深度学习网络与微分同胚数学机制相结合，构建了一个连续且动态的配准架构，该架构由级联在五个不同尺度上的多个对称配准模块组成。具体而言，我们的方法首先使用两个参数共享的U-net从图像中提取多尺度特征金字塔。随后，我们开发了一个包含顺序CNN-LSTM架构的SR模块，通过控制增量学习及同伦延拓技术，逐步校正前向与反向的多尺度形变场。通过在三个3D配准任务上进行的大量实验，我们证明该方法在定量与定性评估上均优于现有方法。