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.

翻译：近年来，深度学习算法在医学图像分割中取得了显著成果。然而，这些网络因参数庞大而难以处理图像边界与细节，导致分割效果不佳。为解决该问题，我们开发了空洞空间金字塔池化（ASPP）并将其与压缩-激励模块（SE block）结合，同时提出PS模块——该模块在网络底层采用更宽的多尺度感受野以获取更精细的语义信息。我们还提出了局部引导模块（LG block），并将其与SE block结合形成LS模块，该模块可在特征图中获取更丰富的局部特征，从而在每次下采样过程中保留更多边缘信息以提升边界分割性能。我们提出PLU-Net，将PS模块与LS模块集成至U-Net中。在三个基准数据集上的实验结果表明，PLU-Net在参数更少、计算量更低的情况下，在医学语义分割任务中表现更优。