Accurate Estimation of Left Ventricle Ejection Fraction Using Fully Convolutional Networks and Fully Connected Conditional Random Field

被引：0
|
作者：
Liu X. [1 ]
Lei Z. [2 ]
He K. [3 ]
Zhang H. [3 ]
Guo S. [1 ]
Zhang X. [1 ]
Li X. [1 ]
机构：
[1] College of Electronic Science and Engineering, Jilin University, Changchun
[2] National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing
[3] Department of Radiology, The First Hospital of Jilin University, Changchun
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 03期
关键词：
Deep learning; Fully connected conditional random field; Fully convolutional networks; Left ventricular ejection fraction;
D O I：
10.3724/SP.J.1089.2019.17216
中图分类号：
学科分类号：
摘要：
Ejection fraction of left ventricle is regarded as an important metric to measure the status of heart. To enhance the accuracy of left ventricle segmentation and ejection fraction estimation, the paper presents a novel framework which bases on improved fully convolutional networks (FCN) and fully connected conditional random field (fc CRF). Firstly, the framework segmented the region of left ventricle from MRI using a pre-trained FCN and obtained probability maps. Secondly, post-processing of pixel-wise label assignment was performed by 3D fc CRF. Finally, the segmentation was reconstructed in 3D; end-systolic volume and end-diastolic volume were acquired, and ejection fraction of left ventricle were then calculated. The results demonstrate the framework can estimate the left ventricular ejection fraction accurately and efficiently; the mean predicted error of left ventricular ejection fraction is 4.67% and the time-consuming is short. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码：431 / 438
页数：7
相关论文
共 27 条
  • [1] Zhang Z.Z., Xie Y.P., Xing F.Y., Et al., MDNet: a semantically and visually interpretable medical image diagnosis network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3549-3557, (2017)
  • [2] Zhang P., Xu X., Wang H., Et al., Computer-aided lung cancer diagnosis approaches based on deep learning, Journal of Computer-Aided Design & Computer Graphics, 30, 1, pp. 90-99, (2018)
  • [3] Wang X., Niu Y., Zhao G., Et al., Combining region proposals and deep SSAE learnt features for detecting left ventricle in cardiac MR images, Journal of Computer-Aided Design & Computer Graphics, 30, 3, pp. 424-435, (2018)
  • [4] Shen W., Zhou M., Yang F., Et al., Multi-scale convolutional neural networks for lung nodule classification, Proceedings of International Conference on Information Processing in Medical Imaging, pp. 588-599, (2015)
  • [5] Nie D., Zhang H., Adeli E., Et al., 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 212-220, (2016)
  • [6] Anthimopoulos M., Christodoulidis S., Ebner L., Et al., Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE Transactions on Medical Imaging, 35, 5, pp. 1207-1216, (2016)
  • [7] Ding J., Li A.X., Hu Z.Q., Et al., Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 559-567, (2017)
  • [8] Shin H.C., Roth H.R., Gao M.C., Et al., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Transactions on Medical Imaging, 35, 5, pp. 1285-1298, (2016)
  • [9] Roth H.R., Lu L., Farag A., Et al., DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 556-564, (2015)
  • [10] Krizhevsky A., Sutskever I., Hinton G.E., ImageNet classification with deep convolutional neural networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, pp. 1097-1105, (2012)
123