  Deep learning algorithms have achieved remarkable results in medical image
segmentation in recent years. These networks are unable to handle with image
boundaries and details with enormous parameters, resulting in poor segmentation
results. To address the issue, we develop atrous spatial pyramid pooling (ASPP)
and combine it with the Squeeze-and-Excitation block (SE block), as well as
present the PS module, which employs a broader and multi-scale receptive field
at the network's bottom to obtain more detailed semantic information. We also
propose the Local Guided block (LG block) and also its combination with the SE
block to form the LS block, which can obtain more abundant local features in
the feature map, so that more edge information can be retained in each down
sampling process, thereby improving the performance of boundary segmentation.
We propose PLU-Net and integrate our PS module and LS block into U-Net. We put
our PLU-Net to the test on three benchmark datasets, and the results show that
by fewer parameters and FLOPs, it outperforms on medical semantic segmentation
tasks.
