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
翻译:有效的骨干网络对于深层学习基于可变化医学图像注册(DMIR)非常重要,因为它提取和匹配了两种图像的特征,以发现相互对应的细细登记。然而,现有的深层网络侧重于单一图像状况,在通过配对图像执行的登记任务方面受到限制。因此,我们推进了一个新型的骨干网络,XMorpher,以在DMIR中有效对应的特征表示。 1)它建议建立一个全新的完整变压器结构,包括双重平行特征提取网络,通过交叉关注交换信息,从而发现多层次的语义通信,同时逐步提取各自的特征,以便最终有效登记。 2)它推进了交叉注意变换(CAT)块,以建立能够自动找到通信的图像之间的关注机制,并促使网络中的有效连接功能。3它限制了基窗口和不同大小的搜索窗口之间的关注度计算,从而同时侧重于可变化登记的地方转换,提高计算效率。在没有任何钟哨的情况下,我们的XMerpher在DSC/M图像上增加了Voxelforfor2.8%的改进。