基于UNet++的自动分割COVID-19病灶模型在CT切片中的应用

Application of UNet++-based automatic segmentation model of COVID-19 lesions in CT slices

针对肺部病变的多样性和区域分割复杂化的问题,提出了一种新的UNet++模型,包括在U-Net基础上进行了改进,主要由挤压和注意模块、空洞空间金字塔池模块、下采样、上采样、跳过连接和损失函数组成.首先,引入了挤压和注意模块来加强像素分组的注意力,充分利用全局上下文信息,让网络更好地挖掘像素之间的差异和联系.其次,设计空洞空间金字塔池模块,用于捕获COVID-19病变的多尺度信息.下采样获得高维信息,然后使用四次上采样将特征图恢复到原始大小,并使用四个跳跃连接来合并特征图.此外,广义骰子损失可以降低病变大小与骰子损失之间的相关性,从而解决小区域分割问题.使用来自不同数据集的CT扫描数据对UNet++模型进行了广泛的实验.在实验中,UNet++模型和GDL分别与典型分割模型和流行的损失函数进行了比较,实验数据表明提出的新的UNet++模型最接近黄金标准.

In view of the diversity of lung lesions and the complexity of regional segmentation,a new UNet++model is proposed,including improvements on the basis of U-Net,mainly composed of extrusion and attention modules,empty space pyramid pooling module,down It consists of sampling,upsampling,skip connections and loss functions.First,squeezing and attention modules are introduced to strengthen the attention of pixel grouping,make full use of global context information,and allow the network to better mine the differences and connections between pixels.Second,a hollow spatial pyramid pooling module is designed to capture multi-scale information of COVID-19 lesions.Downsampling obtains high-dimensional information,then four upsampling is used to restore the feature maps to the original size,and four skip connections are used to merge the feature maps.In addition,the generalized Dice loss can reduce the correlation between lesion size and Dice loss,thus solving the small region segmentation problem.Extensive experiments are performed on the UNet++model using CT scan data from different datasets.In experiments,the UNet++model and GDL are compared with typical segmentation models and popular loss functions,respectively,and the experimental data show that the proposed new UNet++model is closest to the gold standard.