摘要

针对现有方法对肝部医学影像分割上的不足,提出了一种用于对肝部医学影像进行分割的改进型U-Net结构。在上采样过程中只复制池化层特征,以减少信息丢失;同时引入残差网络对初步分割图像进行循环精炼,实现高层特征与低层特征的融合;利用对边界敏感的新型混合损失函数对图像进行细化处理,得到更为精确的分割结果。实验结果表明,肝脏图像和肝脏肿瘤图像的Dice系数分别为96.26%和83.32%。相比传统的U-Net,所提网络可以获得更高级的语义信息,进一步提高对肝脏和肝肿瘤图像的分割精度。

Abstract

To overcome the shortcomings of the existing methods in the segmentation of liver medical images, an improved U-Net structure for liver medical image segmentation is proposed in this paper. To reduce information loss, the pooling layer features are copied during upsampling. Moreover, a residual network is introduced to refine the initial segmented image circularly to combine high-level features with low-level features. Using a new boundary-sensitive mixed loss function to refine the image, the network can obtain more accurate segmentation results. The experimental results show that the Dice coefficients of the liver images and liver tumor images are 96.26% and 83.32%, respectively. Compared with the traditional U-Net, the proposed network can obtain more advanced semantic information and improve the segmentation accuracy of liver and liver tumor images.

补充资料

中图分类号：TP391.4

DOI：10.3788/LOP57.221003

所属栏目：图像处理

基金项目：国家自然科学基金、天津市自然科学基金;

收稿日期：2020-02-21

修改稿日期：2020-03-27

网络出版日期：2020-11-01

作者单位    点击查看

联系人作者：史再峰(shizaifeng@tju.edu.cn)

备注：国家自然科学基金、天津市自然科学基金;

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引用该论文

Huang Yongjia,Shi Zaifeng,Wang Zhongqi,Wang Zhe. Improved U-Net Based on Mixed Loss Function for Liver Medical Image Segmentation[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221003

黄泳嘉,史再峰,王仲琦,王哲. 基于混合损失函数的改进型U-Net肝部医学影像分割方法[J]. 激光与光电子学进展, 2020, 57(22): 221003