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.

该论文介绍了一种轻量级的医学图像分割网络 SegNetr，利用 SegNetr 块在任何阶段动态执行局部 - 全局交互，并通过通用信息保留跳接连接 (IRSC) 有效地融合编码器和解码器的特征，从而在主流医学图像分割数据集上取得了与现有方法相当的分割性能，其组件可应用于改善其他 U 型网络的分割性能。