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.

A lightweight encoder–decoder network for automatic pavement crack detection

“…Lin et al. (2021) proposed a full‐attention U‐Net for improving the segmentation accuracy of crack edges. By integrating the attention gate into each output of the backbone in the architecture to reduce noise at the crack edges, the network can improve the edge detection of steel cracks in large‐scale images.…”