标题： 一种对称动态学习框架用于可微分医学图像配准 显示英文标题

标题： A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration

摘要： 基于微分同胚的图像配准对于各种医学影像应用至关重要，因为它可以保持变换的拓扑结构。 本研究引入了DCCNN-LSTM-Reg，这是一种动态演化的学习框架，通过满足指定的控制增量系统来学习对称配准路径。 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变形。 为了实现这一目标，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，其中包括多个在五个不同尺度上级联的对称配准（SR）模块。 具体而言，我们的方法首先使用两个共享参数的U-Net从图像中提取多尺度特征金字塔。 然后，我们开发了一个由顺序CNN-LSTM架构组成的SR模块，利用控制增量学习和同伦连续技术逐步校正前向和反向多尺度变形场。 通过在三个三维配准任务上的大量实验，我们证明了我们的方法在定量和定性评估中均优于现有方法。 显示英文摘要

摘要： 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.