[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]
点击复制

基于GAN-DAUnet的肝脏CT图像肿瘤分割算法()
分享到：

《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.

相似文献/References:

[1]万俊,聂生东,王远军,等.基于MRI的脑肿瘤分割技术研究进展[J].中国医学物理学杂志,2013,30(04):4266.[doi:10.3969/j.issn.1005-202X.2013.04.013]
[2]林江,戴齐,欧阳婷雪,等.一种边界和马尔可夫随机场相结合的脑MRI医学图像分割方法[J].中国医学物理学杂志,2015,32(05):717.[doi:doi:10.3969/j.issn.1005-202X.2015.05.023]
　[J].Chinese Journal of Medical Physics,2015,32(8):717.[doi:doi:10.3969/j.issn.1005-202X.2015.05.023]
[3]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
　[J].Chinese Journal of Medical Physics,2017,34(8):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[4]门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J].中国医学物理学杂志,2018,35(3):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
　MEN Kuo,DAI Jianrong. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J].Chinese Journal of Medical Physics,2018,35(8):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[5]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
　DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(8):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[6]吴迪,胡胜,刘伟峰,等. 基于特征融合视觉显著性的医学图像分割[J].中国医学物理学杂志,2018,35(6):670.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.010]
　WU Di,HU Sheng,LIU Weifeng,et al. Medical image segmentation based on visual saliency of feature fusion[J].Chinese Journal of Medical Physics,2018,35(8):670.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.010]
[7]刘国才,官文静,田娟秀,等. 集成自适应回归核的肿瘤生物靶区随机游走勾画方法[J].中国医学物理学杂志,2018,35(7):758.[doi:DOI:10.3969/j.issn.1005-202X.2018.07.004]
　LIU Guocai,GUAN Wenjing,TIAN Juanxiu,et al. A random walk method with adaptive regression-kernel for delineation of biological target volumes of tumors[J].Chinese Journal of Medical Physics,2018,35(8):758.[doi:DOI:10.3969/j.issn.1005-202X.2018.07.004]
[8]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
　ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(8):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[9]安莹,黄能军,杨荣,等. 基于深度学习的心血管疾病风险预测模型[J].中国医学物理学杂志,2019,36(9):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
　AN Ying,HUANG Nengjun,YANG Rong,et al. Deep learning-based model for risk prediction of cardiovascular diseases[J].Chinese Journal of Medical Physics,2019,36(8):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[10]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
　XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(8):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[11]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(8):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[12]张富利,杨安宁,路娜,等.基于FC_DenseNet深度学习网络自动分割肺癌放疗中的危及器官[J].中国医学物理学杂志,2021,38(2):259.[doi:DOI:10.3969/j.issn.1005-202X.2021.02.024]
　ZHANG Fuli,YANG Anning,LU Na,et al.FC_Densenet-based autosegmentation of organs-at-risk in lung cancer radiotherapy[J].Chinese Journal of Medical Physics,2021,38(8):259.[doi:DOI:10.3969/j.issn.1005-202X.2021.02.024]
[13]方新林,方艳红,王迪.基于多模态特征融合的脑瘤图像分割方法[J].中国医学物理学杂志,2022,39(6):682.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.005]
　FANG Xinlin,FANG Yanhong,WANG Di.Brain tumor image segmentation method based on multi-modal feature fusion[J].Chinese Journal of Medical Physics,2022,39(8):682.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.005]

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