In recent years, deep learning algorithms have achieved remarkable results in medical image segmentation. These networks with an enormous number of parameters often encounter challenges in handling image boundaries and details, which may result in suboptimal segmentation results. To solve the problem, 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 multiscale 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.
Keywords: U‐Net; deep learning; medical image; semantic segmentation.
© 2023 American Association of Physicists in Medicine.