基于改进UNETR++的肝脏分割

Liver Segmentation Based on Improved UNETR++

肝脏MRI影像的脂肪定量标准化过程中常需要对肝脏感兴趣区域进行手工采样,但手工采样策略耗时且结果多变.基于深度学习方法的全肝分割与手工勾勒的感兴趣区域在进行脂肪定量分析时,变异性误差和不确定性程度更低,性能更优越.在进行全肝分割任务时,为了提升分割性能,本文在UNETR++模型的基础上,进行改进.该方法融合卷积神经网络和Transformer结构各自的优点,增加卷积结构分支用于补足局部特征,同时引入门控注意力机制,抑制不相关的背景信息,使模型更为突出分割区域的显著特征.相比于UNETR++及其他分割模型,改进的方法具有更优的DCS及HD95指标.

In the process of fat quantification standardization in liver MRI images,it is often necessary to manually sample the liver area of interest,but the manual sampling strategy is time-consuming and the results are variable.Compared with manually sketched regions of interest,the whole liver segmentation based on deep learning method has lower variability error and uncertainty,and better performance in fat quantitative analysis.To improve the segmentation performance during the whole liver segmentation task,this study makes improvements based on the UNETR++model.This method combines the advantages of a convolutional neural network and Transformer structure and adds convolutional structure branches to supplement local features.Meanwhile,it introduces a gated attention mechanism to suppress irrelevant background information to make the model more prominent features of the segmented region.The improved method has better DCS and HD95 indexes than UNETR++and other segmentation models.