Semantic Segmentation of Nasal Septum Based on Parameter-Free Attention U-Net

Abstract Accurate segmentation of nasal septum plays a key role in assisting doctors in nasal surgery. However, this practice is still a great challenge due to the variety in the shapes of nasal septum of different people. This paper brought forward an effective parameter-free attention U-Net for accurate segmentation of nasal septum. This attention module is an energy function, which is used to identify the importance of each pixel and provide three-dimensional attention weight for feature map inference in the layer without any additional parameters. On this basis, a new loss function of poly-diceloss was introduced, which regarded the diceloss as a linear combination of polynomial functions and significantly improved the segmentation performance by introducing a super parameter. In this paper, a data set named “nasal septum” was constructed. Based on this data set, a comparison was made with the most advanced network and the experimental results showed that the indexes of SAMU-Net proposed in this paper were: \(TPR\) 98.47%, \(PPV\) 83.57%, \(JAC\) 82.37%, \(Dice\) 90.23%, respectively, using the least network parameters.