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。

SegNetr: 重新思考 U 型网络中的局部 - 全局交互和跳过连接

SegNetr: Rethinking the local-global interactions and skip connections  in U-shaped networks

U-shaped networks and its variants have demonstrated exceptional results for
medical image segmentation. In this paper, we propose a novel dual
self-distillation (DSD) framework for U-shaped networks for 3D medical image
segmentation. DSD distills knowledge from the ground-truth segmentation labels
to the decoder layers and also between the encoder and decoder layers of a
single U-shaped network. DSD is a generalized training strategy that could be
attached to the backbone architecture of any U-shaped network to further
improve its segmentation performance. We attached DSD on two state-of-the-art
U-shaped backbones, and extensive experiments on two public 3D medical image
segmentation datasets (cardiac substructure and brain tumor) demonstrated
significant improvement over those backbones. On average, after attaching DSD
to the U-shaped backbones, we observed an improvement of 4.25% and 3.15% in
Dice similarity score for cardiac substructure and brain tumor segmentation
respectively.

本文提出了一种新颖的双自蒸馏（DSD）框架，该框架适用于 U 形网络进行三维医学图像分割，通过在 U 形网络的背骨架构上附加 DSD，有效地提高了分割性能，其中 Dice 相似度分数提高了 4.25％至 3.15％