MSA-Net: Multiscale spatial attention network for medical image segmentation

Edge accuracy and positional accuracy are the two goals pursued by medical image segmentation. In clinical medicine diagnosis and research, these two goals enable medical image segmentation techniques to help in the effective determination of lesions and lesion analysis. At present, U-Net has become the most important network in the field of image segmentation, and the technologies used in various achievements are derived from its architecture, which also proves from practice that the network structure proposed by U-Net is effective. We have found in a large number of experiments that classical networks indeed show good performance in the field of medical segmentation, but there are still some deficiencies in edge determination and network robustness, especially in the face of blurred edges, the processing results often fail to achieve the expected results. In order to be able to locate segmentation targets and achieve effective determination of blurred edges, a Multiscale Spatial Attention Network (MSA-Net) is proposed as in Fig. 1. In MSA-Net, the Multiscale Pyramid Attention Block (MPAB) is created to enhance the capture of high-level semantic information. In addition, the network uses ASPP, which not only expands the network’s field of view, but also captures richer feature information. In the decoding phase, the Feature Fusion Block (FFB) is created to enable better focus on different dimensional information features and to enhance the feature fusion process. To demonstrate the effectiveness of the network, we validate the performance of MSA-Net on four datasets (ISIC2016, DSB2018, JSRT, GlaS) in three different categories. Compared with mainstream networks, MSA-Net shows better results in detail features, target localization, and edge processing. Finally, we also demonstrate the effectiveness of the MSA-Net architecture through ablation experiments.