[1]周金治,胡震,郭莉莉,等.基于GAN-DAUnet的肝脏CT图像肿瘤分割算法[J].中国医学物理学杂志,2023,40(8):971-976.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.008]
　ZHOU Jinzhi,HU Zhen,et al.Liver CT image tumor segmentation algorithm based on GAN-DAUnet[J].Chinese Journal of Medical Physics,2023,40(8):971-976.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.008]
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基于GAN-DAUnet的肝脏CT图像肿瘤分割算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第8期
页码:
971-976
栏目:
医学影像物理
出版日期:
2023-09-01

文章信息/Info

Title:
Liver CT image tumor segmentation algorithm based on GAN-DAUnet
文章编号:
1005-202X（2023）08-0971-06
作者:
周金治1; 2; 胡震1; 2; 郭莉莉1; 2; 龚莉1; 2; 张翁荣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):
ZHOU Jinzhi1;  2;  HU Zhen1;  2;  GUO Lili1;  2;  GONG Li1;  2;  ZHANG Wengrong1;  2
1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. Sichuan Provincial Key Laboratory of Robot Technology Used for Special Environment, Mianyang 621000, China
关键词:
医学图像分割; 深度学习; 生成对抗网络; 注意力机制; 肝脏肿瘤
Keywords:
medical image segmentation deep learning generative adversarial network attention mechanism liver tumor
分类号:
R318；R735.7
DOI:
DOI:10.3969/j.issn.1005-202X.2023.08.008
文献标志码:
A
摘要:
针对现有肝脏CT图像肿瘤分割方法中存在欠分割、过分割、边界模糊以及分割精度较低的问题，提出一种基于生成对抗网络（GAN）的肝脏肿瘤自动分割算法。该算法首先对图像进行预分割减少无关信息的影响。其次GAN的生成网络使用DAUnet，该网络在跳跃连接中引入双注意力机制增强肝脏肿瘤的特征。最后通过GAN的生成对抗训练并在训练过程中引入混合损失函数，使预测的肿瘤图像更加精准。在LiTS数据集上的实验结果表明，提出的算法Dice相似系数值（Dice similarity coefficient, DSC）达到了76.15%，相比于Unet提升3.63%。通过对DAUnet进行生成对抗训练能有效提高肝脏图像中肿瘤的分割性能。
Abstract:
Aiming at the problems of under-segmentation, over-segmentation, boundary ambiguity, and low segmentation accuracy in the existing liver CT image segmentation methods, an automatic segmentation algorithm for liver tumors based on a generative adversarial network (GAN) is proposed. Firstly, the image is pre-segmented to reduce the effect of irrelevant information. Secondly, the GAN-generative network uses Dual Attention Unet (DAUnet), which introduces dual attention mechanisms in skip connections to enhance the features of liver tumors. Finally, the predicted tumor images become more accurate through the generative adversarial training of GAN and the introduction of the mixed loss function in the training process. The experimental results on the LiTS dataset show that the Dice similarity coefficient of the proposed algorithm reaches 76.15%, which is 3.63% higher than that of Unet. DAUnet for generative adversarial training can effectively improve the performance of tumor segmentation in liver images.

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备注/Memo

备注/Memo:
【收稿日期】2023-02-13 【基金项目】国家自然科学基金（61771411） 【作者简介】周金治，硕士，副教授，主要从事信号处理、通信网络、机器学习等方面的教学与科研工作，E-mail: zhoujinzhi@swust.edu.cn
更新日期/Last Update: 2023-09-06