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的特性，在医学图像分割领域占据主导地位。然而，现有U形分割网络存在以下问题：1）多数研究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，导致网络参数量与计算复杂度整体增加；2）编码器与解码器特征仅进行简单融合，忽略了两者空间位置间的关联。本文针对上述问题展开重新思考，构建了一种轻量级医学图像分割网络SegNetr。具体而言，我们提出新型SegNetr模块——该模块能以线性复杂度在任意阶段动态实现局部-全局交互；同时设计通用型信息保持跳跃连接（IRSC），用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准融合。在四个主流医学图像分割数据集上的实验验证了SegNetr的有效性：相比原始U-Net，参数量降低59%，GFLOPs降低76%，同时分割性能可媲美当前最先进方法。值得注意的是，本文提出的组件可迁移应用于其他U形网络以提升其分割性能。