标题： 基于有效解剖结构表示和分而治之网络的可变形医学图像配准 显示英文标题

标题： Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network

摘要： 有效表示感兴趣区域（ROI）并独立对齐这些ROI可以显著提高可变形医学图像配准（DMIR）的性能。然而，当前基于学习的DMIR方法存在局限性。无监督技术忽略ROI表示，直接对图像对进行对齐，而弱监督方法则严重依赖标签约束来促进配准。为了解决这些问题，我们引入了一种名为EASR-DCN的新ROI基础配准方法。我们的方法通过有效的ROI表示医学图像，并在不需要标签的情况下实现这些ROI的独立对齐。具体来说，我们首先使用高斯混合模型进行强度分析，以具有不同强度的多个有效ROI来表示图像。此外，我们提出了一种新的分治网络（DCN），通过单独的通道处理这些ROI，以学习每个ROI的特征对齐。产生的对应关系被无缝集成以生成全面的位移向量场。在三个MRI和一个CT数据集上进行了大量实验，以展示我们EASR-DCN的优越准确性和变形减少效果。与VoxelMorph相比，我们的EASR-DCN在脑部MRI的Dice分数上提高了10.31%，在心脏MRI上提高了13.01%，在海马体MRI上提高了5.75%，突显了其在临床应用中的前景。本文的代码将在论文接受后发布。 显示英文摘要

摘要： Effective representation of Regions of Interest (ROI) and independent alignment of these ROIs can significantly enhance the performance of deformable medical image registration (DMIR). However, current learning-based DMIR methods have limitations. Unsupervised techniques disregard ROI representation and proceed directly with aligning pairs of images, while weakly-supervised methods heavily depend on label constraints to facilitate registration. To address these issues, we introduce a novel ROI-based registration approach named EASR-DCN. Our method represents medical images through effective ROIs and achieves independent alignment of these ROIs without requiring labels. Specifically, we first used a Gaussian mixture model for intensity analysis to represent images using multiple effective ROIs with distinct intensities. Furthermore, we propose a novel Divide-and-Conquer Network (DCN) to process these ROIs through separate channels to learn feature alignments for each ROI. The resultant correspondences are seamlessly integrated to generate a comprehensive displacement vector field. Extensive experiments were performed on three MRI and one CT datasets to showcase the superior accuracy and deformation reduction efficacy of our EASR-DCN. Compared to VoxelMorph, our EASR-DCN achieved improvements of 10.31\% in the Dice score for brain MRI, 13.01\% for cardiac MRI, and 5.75\% for hippocampus MRI, highlighting its promising potential for clinical applications. The code for this work will be released upon acceptance of the paper.