Contact Us Search Paper

Deep Learning Methods for Cardiovascular Image

Yankun Cao1, Zhi Liu1, *, Pengfei Zhang2, Yushuo Zheng3, Yongsheng Song4, Lizhen Cui5

Corresponding Author:

Zhi Liu

Affiliation(s):

1 School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
2 Qilu Hospital, Shandong University, Jinan, 250014, China.
3 High School Attached to Shandong Normal University, Jinan, 250014, China.
4 Kedun Science and Technology Co., Ltd., Laiyang, 265200, China.
5 School of Software, Shandong University, Jinan, 250001, China.
*Corresponding Author: Zhi Liu ([email protected])

Abstract:

In the medical field, the analysis and processing of medical images plays an important auxiliary role in the diagnosis of diseases. In recent years, more and more researchers have begun to pay attention to such processing technologies as pattern recognition, classification and segmentation in medical image processing. Cardiovascular disease is one of the most important diseases that endanger human health at present. It is very meaningful to diagnose and treat cardiovascular disease by means of in-depth learning. In order to make deep learning better applied to cardiovascular diseases, this paper first outlines the development and causes of cardiovascular diseases, then describes several theoretical models of deep learning, and then summarizes the application of deep learning in heart image segmentation, classification and other aspects combined with existing technologies. Finally, the future direction of development is prospected.

Keywords:

Deep learning, Cardiovascular Diseases, cardiac imaging

Downloads: 692 Views: 4410
Cite This Paper:

Yankun Cao; Zhi Liu; Pengfei Zhang; Yushuo Zheng; Yongsheng Song; Lizhen Cui (2019). Deep Learning Methods for Cardiovascular Image. Journal of Artificial Intelligence and Systems, 1, 96–109. https://doi.org/10.33969/AIS.2019.11006.

References:

[1] World health statistics 2018: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO
[2] Wong K K L, Tu J, Sun Z, et al. Methods in research and development of biomedical devices[M]. WORLD SCIENTIFIC, 2013.
[3] MacNeill, B. D. Intravascular Modalities for Detection of Vulnerable Plaque: Current Status [J]. Arteriosclerosis, Thrombosis, and Vascular Biology, 2003, 23(8):1333-1342.
[4] Kips J G, Segers P , Bortel L M V . Identifying the vulnerable plaque: A review of invasive and non-invasive imaging modalities[J]. Artery Research, 2008, 2(1):21-34.
[5] Jaffer F A, Libby P , Weissleder R . Optical and Multimodality Molecular Imaging: Insights Into Atherosclerosis [J]. Arteriosclerosis, Thrombosis, and Vascular Biology, 2009, 29(7):1017-1024.
[6] Anthimopoulos M, Christodoulidis S, Ebner L, et al. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[J]. IEEE transactions on medical imaging, 2016, 35(5): 1207-1216.
[7] Miao S, Wang Z J, Liao R. A convolutional neural network approach for 2d/3d medical image registration[J]. CoRR abs/1507.07505, 2015.
[8] Vardhana M, Arunkumar N, Lasrado S, et al. Convolutional neural network for bio-medical image segmentation with hardware acceleration[J]. Cognitive Systems Research, 2018, 50: 10-14.
[9] K. J. Xia, H. S. Yin, J. Q. Wang, “A novel improved deep convolutional neural network model for medical image fusion,” Cluster Computing, 2018, 3:1-13.
[10] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[11] Xiao Z, Huang R, Ding Y, et al. A deep learning-based segmentation method for brain tumor in MR images[C]// IEEE, International Conference on Computational Advances in Bio and Medical Sciences. IEEE, 2017:1-6.
[12] Jiang Z, Lin Z, Davis L S. Label Consistent K-SVD: Learning A Discriminative Dictionary for Recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(11):2651-64.
[13] Zheng M, Bu J, Chen C, et al. Graph Regularized Sparse Coding for Image Representation[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2011, 20(5):1327.
[14] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]. Advances in neural information processing systems, 2012: 1097-1105.
[15] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
[16] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv: 1409.1556, 2014.
[17] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016: 770-778.
[18] Caffe [Online]. Available: http://caffe.berkeleyvision.org/.(Accessed on 24 May 2016)
[19] Tensorflow [Online]. Available: https://tensorflow.org/. (Accessed on 2016)
[20] Pytorch [Online]. Available: https://pytorch.org/.  (Accessed on 2017)
[21] Poudel R P K, Lamata P, Montana G. Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation[M]//Reconstruction, segmentation, and analysis of medical images. Springer, Cham, 2016: 83-94.
[22] Bai W, Oktay O, Sinclair M, et al. Semi-supervised learning for network-based cardiac MR image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2017: 253-260.
[23] Baumgartner C F, Koch L M, Pollefeys M, et al. An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation[C]//International Workshop on Statistical Atlases and Computational Models of the Heart. Springer, Cham, 2017: 111-119.
[24] Oktay O, Ferrante E, Kamnitsas K, et al. Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation[J]. IEEE transactions on medical imaging, 2017, 37(2): 384-395.
[25] Chang Y, Song B, Jung C, et al. Automatic Segmentation and Cardiopathy Classification in Cardiac Mri Images Based on Deep Neural Networks[C]//2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018: 1020-1024.
[26] Kotu L P, Engan K, Borhani R, et al. Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients[J]. Artificial intelligence in medicine, 2015, 64(3): 205-215.
[27] Gan Y, Tsay D, Amir S B, et al. Automated classification of optical coherence tomography images of human atrial tissue[J]. Journal of biomedical optics, 2016, 21(10): 101407.
[28] Ghaemmaghami H, Hussain N, Tran K, et al. Automatic segmentation and classification of cardiac cycles using deep learning and a wireless electronic stethoscope[C]//2017 IEEE Life Sciences Conference (LSC). IEEE, 2017: 210-213.
[29] Gao X, Li W, Loomes M, et al. A fused deep learning architecture for viewpoint classification of echocardiography[J]. Information Fusion, 2017, 36: 103-113.
[30] Madani A, Arnaout R, Mofrad M, et al. Fast and accurate view classification of echocardiograms using deep learning[J]. NPJ digital medicine, 2018, 1(1): 6.
[31] Isensee F, Jaeger P F, Full P M, et al. Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features[C]//International workshop on statistical atlases and computational models of the heart. Springer, Cham, 2017: 120-129.
[32] Rohé M M, Datar M, Heimann T, et al. SVF-Net: Learning deformable image registration using shape matching[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2017: 266-274.
[33] Gao Z, Xiong H, Liu X, et al. Robust estimation of carotid artery wall motion using the elasticity-based state-space approach[J]. Medical image analysis, 2017, 37: 1-21.
[34] Zhen X, Zhang H, Islam A, et al. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression[J]. Medical image analysis, 2017, 36: 184-196.
[35] Gao Z Zhao S, Gao Z, Zhang H, et al. Robust Segmentation of Intima–Media Borders With Different Morphologies and Dynamics During the Cardiac Cycle[J]. IEEE journal of biomedical and health informatics, 2017, 22(5): 1571-1582.
[36] Gao Z, Wu S, Liu Z, et al. Learning the implicit strain reconstruction in ultrasound elastography using privileged information[J]. Medical image analysis, 2019, 58: 101534.
[37] Xu L, Huang X, Ma J, et al. Value of three-dimensional strain parameters for predicting left ventricular remodeling after ST-elevation myocardial infarction[J]. The International Journal of Cardiovascular Imaging, 2017, 33(5):663-673.
[38] Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals[J]. Expert Systems with Applications, 2013, 40(8):3096-3105.
[39] Khamparia A, Saini G, Gupta D, et al. Seasonal Crops Disease Prediction and Classification Using Deep Convolutional Encoder Network[J]. Circuits, Systems, and Signal Processing, 2019(May).
[40] Albuquerque V H C D, Nunes T M, Pereira D R, et al. Robust automated cardiac arrhythmia detection in ECG beat signals[J]. Neural Computing & Applications, 2018, 29(3):1-15.