一种具有边缘增强特点的医学图像分割网络

针对传统医学图像分割网络存在边缘分割不清晰、缺失值大等问题，该文提出一种具有边缘增强特点的医学图像分割网络(AS-UNet)。利用掩膜边缘提取算法得到掩膜边缘图，在UNet扩张路径的最后3层引入结合多尺度特征图的边缘注意模块(BAB)，并提出组合损失函数来提高分割精度；测试时通过舍弃BAB来减少参数。在3种不同类型的医学图像分割数据集Glas, DRIVE, ISIC2018上进行实验，与其他分割方法相比，AS-UNet分割性能较优。

A Medical Image Segmentation Network with Boundary Enhancement

A medical image segmentation network with boundary enhancement, named as the AS-UNet (Add-and-Subtract UNet), is proposed to solve the problems of traditional segmentation networks for medical images, such as unclear boundary segmentation and large missing value. The mask boundary image is obtained by using the mask boundary image extraction algorithm, and the Boundary Attention Block (BAB) with multi-scale feature maps is introduced into the last three layers of the UNet expansion path. Moreover, the combinatorial loss function is proposed to improve the segmentation accuracy. In testing, the BAB can be abandoned to reduce testing parameters. Comparisons with other segmentation methods on three different types of medical image segmentation datasets, Glas, DRIVE and ISIC2018 are provided, indicating that the segmentation performance of the AS-UNet is better.