前进 (2022-10-30 21:26):
#paper Shi J, He Y, Kong Y, et al. XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2022: 217-226.现有的深度网络专注于单个图像的特征提取,并且在对成对图像执行的配准任务方面受到限制。因此,本文提出了一种新的骨干网络XMorpher,有效地对变形配准中成对特征进行表示。1) 它提出了一种新的Transformer架构,包括双并行特征提取网络,该网络通过Cross Attention来改变信息,从而发现多级语义对应关系,同时逐步提取各自的特征,以实现最终的有效配准。2) 它提出了Cross Attention Transformer(CAT)块,以建立图像之间的注意力机制,该机制能够自动找到对应关系,并促使特征在网络中有效融合。3) 它限制了不同大小的基本窗口和搜索窗口之间的计算,从而集中于可变形配准的局部变换,同时提高了计算效率。XMorpher使Voxelmorph在DSC上提高了2.8%,证明了其在变形配准中对配对图像的特征的有效表示。
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
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Abstract:
An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration. However, the existing deep networks focus on single image situation and are limited in registration task which is performed on paired images. Therefore, we advance a novel backbone network, XMorpher, for the effective corresponding feature representation in DMIR. 1) It proposes a novel full transformer architecture including dual parallel feature extraction networks which exchange information through cross attention, thus discovering multi-level semantic correspondence while extracting respective features gradually for final effective registration. 2) It advances the Cross Attention Transformer (CAT) blocks to establish the attention mechanism between images which is able to find the correspondence automatically and prompts the features to fuse efficiently in the network. 3) It constrains the attention computation between base windows and searching windows with different sizes, and thus focuses on the local transformation of deformable registration and enhances the computing efficiency at the same time. Without any bells and whistles, our XMorpher gives Voxelmorph 2.8% improvement on DSC, demonstrating its effective representation of the features from the paired images in DMIR. We believe that our XMorpher has great application potential in more paired medical images. Our XMorpher is open on https://github.com/Solemoon/XMorpher
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