基于融合U-Net和水平集的肝脏CT图像分割  

Liver CT image segmentation based on fusion of U-Net and level set

作　　者：张宇豪 徐磊[1] 白一清 Zhang Yuhao;Xu Lei;Bai Yiqing(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子测量技术》2021年第9期116-121,共6页Electronic Measurement Technology

摘　　要：为了弥补U-Net模型和水平集方法分割肝脏CT图片的不足之处,提出了一种将两者进行融合的分割算法。该算法在训练阶段使用U-Net模型作为先验网络对肝脏进行分割,然后将其输出的分割结果作为先验特征图送入到水平集方法中进一步分割;通过计算两次分割结果的差异,反向传播误差,更新网络参数,最终得到一个完整的分割算法模型。通过在公开数据集3Dircadb上进行实验对比,所提出分割算法的灵敏性和Dice系数的平均值分别可以达到94.85%和95.18%,面积重叠误差和相对面积误差的平均值仅有9.97%和7.69%。与其他常见的分割算法相比,该算法获得了更好的分割结果。In order to make up for the shortcomings of the U-Net model and the level set method in segmenting liver CT images,this paper proposes a segmentation algorithm which combines U-Net model with the level set method.The algorithm uses the U-Net model as a prior network to segment the liver in the training phase,and then sends its output segmentation result as a prior feature map to the level set method for further segmentation;by calculating the difference between the two segmentation results,back propagation error,update network parameters,and finally get a complete segmentation algorithm model.Through experimental comparisons on the public data set 3Dircadb,this paper proposes that the sensitivity of the segmentation algorithm and the average Dice coefficient can reach 94.85%and 95.18%,respectively,and the average area overlap error and relative area error are only 9.97%and 7.69%.Compared with other common segmentation algorithms,this algorithm obtains better segmentation results.

关 键 词：U-Net 水平集方法 先验网络 先验特征图 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]