  Models based on U-like structures have improved the performance of medical
image segmentation. However, the single-layer decoder structure of U-Net is too
"thin" to exploit enough information, resulting in large semantic differences
between the encoder and decoder parts. Things get worse if the number of
training sets of data is not sufficiently large, which is common in medical
image processing tasks where annotated data are more difficult to obtain than
other tasks. Based on this observation, we propose a novel U-Net model named
MS-UNet for the medical image segmentation task in this study. Instead of the
single-layer U-Net decoder structure used in Swin-UNet and TransUnet, we
specifically design a multi-scale nested decoder based on the Swin Transformer
for U-Net. The proposed multi-scale nested decoder structure allows the feature
mapping between the decoder and encoder to be semantically closer, thus
enabling the network to learn more detailed features. In addition, we propose a
novel edge loss and a plug-and-play fine-tuning Denoising module, which not
only effectively improves the segmentation performance of MS-UNet, but could
also be applied to other models individually. Experimental results show that
MS-UNet could effectively improve the network performance with more efficient
feature learning capability and exhibit more advanced performance, especially
in the extreme case with a small amount of training data, and the proposed Edge
loss and Denoising module could significantly enhance the segmentation
performance of MS-UNet.
