Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. However, existing U-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network; 2) simply fuse the features of encoder and decoder, ignoring the connection between their spatial locations. In this paper, we rethink the above problem and build a lightweight medical image segmentation network, called SegNetr. Specifically, we introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. At the same time, we design a general information retention skip connection (IRSC) to preserve the spatial location information of encoder features and achieve accurate fusion with the decoder features. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59\% and 76\% fewer parameters and GFLOPs than vanilla U-Net, while achieving segmentation performance comparable to state-of-the-art methods. Notably, the components proposed in this paper can be applied to other U-shaped networks to improve their segmentation performance.

翻译：近年来，U形网络因其结构简单且易于调参而主导了医学图像分割领域。然而，现有的U形分割网络：1）大多聚焦于设计复杂的自注意力模块以弥补基于卷积操作的长期依赖缺失，这增加了网络的整体参数量和计算复杂度；2）简单融合编码器与解码器的特征，忽略了其空间位置间的关联。本文重新思考上述问题，构建了一种轻量级医学图像分割网络——SegNetr。具体而言，我们引入了一种新颖的SegNetr块，该块能在任意阶段以线性复杂度动态执行局部-全局交互。同时，我们设计了通用信息保留跳跃连接（IRSC），以保留编码器特征的空间位置信息并实现与解码器特征的精确融合。我们在四个主流医学图像分割数据集上验证了SegNetr的有效性：相比原始U-Net，其参数量和GFLOPs分别减少59%和76%，同时达到了与先进方法相当的分割性能。值得注意的是，本文提出的组件可应用于其他U形网络以提升其分割性能。