[1]莫春梅,周金治,李雪,等.基于改进U-Net的肝脏分割方法[J].中国医学物理学杂志,2021,38(5):571-577.[doi:DOI:10.3969/j.issn.1005-202X.2021.05.009]
　MO Chunmei,ZHOU Jinzhi,et al.Liver segmentation method based on improved U-Net[J].Chinese Journal of Medical Physics,2021,38(5):571-577.[doi:DOI:10.3969/j.issn.1005-202X.2021.05.009]
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基于改进U-Net的肝脏分割方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第5期
页码:
571-577
栏目:
医学影像物理
出版日期:
2021-05-01

文章信息/Info

Title:
Liver segmentation method based on improved U-Net
文章编号:
1005-202X（2021）05-0571-07
作者:
莫春梅1; 2; 周金治1; 2; 李雪1; 2; 余玺1; 2
1.西南科技大学信息工程学院， 四川 绵阳 621000； 2.特殊环境机器人技术四川省重点实验室， 四川 绵阳 621000
Author(s):
MO Chunmei1;  2;  ZHOU Jinzhi1;  2;  LI Xue1;  2;  YU Xi1;  2
1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621000, China
关键词:
肝脏分割; U-Net; 残差模块; 跳跃连接; 混合损失函数
Keywords:
Keywords: liver segmentation U-Net residual block skip connection mixed loss function
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2021.05.009
文献标志码:
A
摘要:
针对现有肝脏图像分割方法存在分割精度较低的问题，提出一种改进U-Net的肝脏分割方法。该方法对U-Net结构做出以下改进，即引入改进的残差模块、重新设计跳跃连接，然后采用混合损失函数，从而提高特征信息的利用率，减少编码器和解码器之间的语义差异，缓解类不平衡的问题并且加快网络收敛。在CodaLab组织提供的公共数据集LITS（Liver Tumor Segmentation）上的实验结果表明，利用该方法达到的Dice相似系数值、敏感度、交并比分别为93.69%、94.87%和87.49%。相比于U-Net和Attention U-Net等分割方法，该方法分割出的肝脏区域结果更加准确，取得了更好的分割性能。
Abstract:
Abstract: In order to solve the problem of low precision in existing methods for liver segmentation, a liver segmentation method based on improved U-Net is proposed. U-Net structure is improved by introducing improved residual block and redesigning skip connection, and then mixed loss function is adopted to enhance the utilization of feature information and reduce the semantic differences between encoder and decoder, thereby alleviating class imbalance problem and speeding up network convergence. The experimental results on Liver Tumor Segmentation (LITS), a common data set provided by CodaLab, showed that the Dice similarity coefficient, sensitivity and intersection over union achieved by the proposed method were 93.69%, 94.87% and 87.49%, respectively. Compared with other segmentation methods, such as U-Net and Attention U-Net, the proposed method can obtain a more accurate result in liver segmentation and has better segmentation performance.

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备注/Memo

备注/Memo:
【收稿日期】2021-01-18 【基金项目】国家自然科学基金（11472297）；西南科技大学研究生创新基金（20ycx0056） 【作者简介】莫春梅，硕士，研究方向：医学图像处理、机器学习，E-mail: 1466567410@qq.com； 【通信作者】周金治，硕士，副教授，研究方向：智能信息处理、机器学习，E-mail: 7467644@qq.com
更新日期/Last Update: 2021-05-31