Deformable medical image registration based on unsupervised generative adversarial network integrating dual attention mechanisms

Meng Li, Yuwen Wang, Fuchun Zhang, Guoqiang Li, Shunbo Hu, Liang Wu
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引用次数: 2

Abstract

Registration is a basic subject of medical image research, and has been a research hotspot for decades. In the process of optimizing each pair of images, the traditional registration requires a lot of calculation, which is very time-consuming for a large amount of data. In recent years, the existing deep learning network framework, especially the model based on U-Net structure, has not only improved the computing speed, but also greatly improved the registration performance. However, the feature loss occurs in the UpSampling process of this structure. Hence, We propose a generative adversarial network using a dual attention mechanisms without any supervised information. In UpSampling process of the registration network, the dual attention mechanism is introduced to improve feature recovery ability. The dual attention mechanism consists of channel attention mechanism and location attention mechanism. For the registration network, local crosscorrelation loss functions are proposed to improve image similarity. Experiments show that our method has achieved perfect registration effect, especially in the edge region.
基于双注意机制的无监督生成对抗网络的形变医学图像配准
配准是医学图像研究的基础学科,几十年来一直是研究热点。在对每对图像进行优化的过程中,传统的配准需要进行大量的计算,对于大量的数据来说,这是非常耗时的。近年来,现有的深度学习网络框架,特别是基于U-Net结构的模型,不仅提高了计算速度,而且极大地提高了配准性能。但是,这种结构的UpSampling过程中会出现特征丢失。因此,我们提出了一个使用双重注意机制的生成对抗网络,没有任何监督信息。在配准网络的UpSampling过程中,引入双注意机制,提高特征恢复能力。双重注意机制包括通道注意机制和位置注意机制。对于配准网络,提出了局部互相关损失函数来提高图像相似度。实验表明,该方法取得了较好的配准效果,特别是在边缘区域。
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