[1]苑金辉,乔 艳,费烨琳,等.基于深度迁移学习的心脏 MRI 图像左心室分割[J].计算机技术与发展,2021,31(06):35-39.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 007]
　YUAN Jin-hui,QIAO Yan,FEI Ye-lin,et al.Left Ventricular Segmentation in Cardiac MRI Images Based onDeep Transfer Learning[J].,2021,31(06):35-39.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 007]
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基于深度迁移学习的心脏 MRI 图像左心室分割()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年06期
页码:
35-39
栏目:
图形与图像
出版日期:
2021-06-10

文章信息/Info

Title:
Left Ventricular Segmentation in Cardiac MRI Images Based onDeep Transfer Learning
文章编号:
1673-629X(2021)06-0035-05
作者:
苑金辉1 ; 乔 艳2 ; 费烨琳2 ; 胡晓飞2
1. 南京邮电大学 通信与信息工程学院,江苏 南京 210003;
2. 南京邮电大学 地理与生物信息学院,江苏 南京 210003
Author(s):
YUAN Jin-hui1 ; QIAO Yan2 ; FEI Ye-lin2 ; HU Xiao-fei2
1. School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications, Nanjing 210003,China;
2. School of Geography and Bioinformatics,Nanjing University of Posts and Telecommunications,? ? ?Nanjing 210003,China
关键词:
迁移学习; 生成对抗网络; 心脏 MRI; 左心室分割; 多尺度
Keywords:
transfer learning; generative adversarial networks; cardiac MRI; left ventricular segmentation; multiscale
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 06. 007
摘要:
在研究基于深度学习的左心室分割方法时,需要足够的有标注的图像,才能获得准确的分割结果,而有标注的左心室图像往往难以获得。因此,提出了一种基于迁移学习和多尺度判别的生成对抗网络(TLMDB GAN) 的 MRI 左心室图像分割方法,解决心室图像数据不足的问题。 模型包含一个分割网络和一个判别网络。 分割网络? ? ?(TLBSN) 使用全卷积神经网络,利用迁移学习逐层微调辅助分割,判别网络是一个多尺度的判别网络,监督生成网络更好地学习图像的特征信息。 实验结果表明,基于多伦多市儿童病医院影像科提供的数据集对左心室内膜和外膜分割 Dice 相似系数分别为 0. 939 9和 0. 969 7。 对比其他分割模型,该模型明显提高了分割精度。
Abstract:
In the study of left ventricular segmentation based on deep learning,sufficient labeled images are needed? to obtain accurate segmentation results,while labeled left ventricular images are often difficult to obtain. Therefore,a left ventricular MRI image segmentation method based on transfer learning and generation confrontation network is proposed to solve the problem of insufficient ventricular image data. The model consists of a segmentation network and a discrimination network. The segmentation network uses the full convolution neural network and the transfer learning to fine-tune the auxiliary segmentation layer by layer. The discriminant network is a multi-scale discriminant network,and the supervised generation network can learn the feature information of the image better. The experiment show that the Dice similarity coefficients of left ventricular endocardium and epicardium segmentation are 0. 939 9 and 0. 969 7,respectively,based on the data set provided by the Imaging Department of Sick Children’s Hospital of Toronto. Compared with other models,the proposed model has significantly improved the segmentation accuracy.

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更新日期/Last Update: 2021-06-10