前进 (2022-11-28 10:25):
#paper Zhu Y , Lu S . Swin-VoxelMorph: A Symmetric Unsupervised Learning Model forDeformable Medical Image Registration Using Swin Transformer[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2022. 可变形医学图像配准广泛应用于医学图像处理中,具有可逆一对一的映射。虽然最先进的图像配准方法是基于卷积神经网络,但很少有人尝试用Transformer的方法。现有的模型忽略了在嵌入学习中使用注意机制来处理远程交叉图,限制了这种方法来识别解剖结构的语义上有意义的对应关系。这些方法虽然实现了快速的图像配准,但也忽略了变换的拓扑保存和可逆性。在本文中,提出了一种新的基于Swin Transformer对称无监督学习网络,它可以最小化图像之间的差异,并同时估计正变换和逆变换像相关性.具体地说,本文提出了三维Swin-UNet,它应用具有Shfited window的分层Swin Transformer作为编码器来提取上下文特征。设计了一种基于patch expanding的symmetric swin Transformer解码器,进行上采样操作,估计配准场。此外,目标损失函数可以保证预测变换的实质性微分性质。本文在ADNI和PPMI两个数据集上验证了该方法,并在保持理想的微分性质的同时实现了最先进的配准精度。
Swin-VoxelMorph: A Symmetric Unsupervised Learning Model for Deformable Medical Image Registration Using Swin Transformer
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Abstract:
Deformable medical image registration is widely used in medical image processing with the invertible and one-to-one mapping between images. While state-of-the-art image registration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on computer vision tasks. Existing models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. These methods also ignore the topology preservation and invertibility of the transformation although they achieve fast image registration. In this paper, we propose a novel, symmetric unsupervised learning network Swin-VoxelMorph based on the Swin Transformer which minimizes the dissimilarity between images and estimates both forward and inverse transformations simultaneously. Specifically, we propose 3D Swin-UNet, which applies hierarchical Swin Transformer with shifted windows as the encoder to extract context features. And a symmetric Swin Transformer-based decoder with patch expanding layer is designed to perform the up-sampling operation to estimate the registration fields. Besides, our objective loss functions can guarantee substantial diffeomorphic properties of the predicted transformations. We verify our method on two datasets including ADNI and PPMI, and it achieves state-of-the-art registration accuracy while maintaining desirable diffeomorphic properties.
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