ABSTRACT
Congenital heart defects is one of the most common neonatal diseases and has a very low survival rate. The fetal heart is generally smaller and possesses a faster than normal beating rate, thus making medical diagnosis difficult. The efficiency and accuracy of diagnosis of congenital heart disease can be improved by computer-aided diagnostic methods. Optical flow is a robust algorithm for object recognition and motion detection, and has potential in early detection of congenital heart defects. In this paper, an end-to-end deep learning system is proposed for obtaining the optical flow information from 4D fetal cardiac ultrasound images. The optical flow network model is trained by using gradients of image sequences obtained from a virtual data set. Subsequently, the trained model is used to detect the cardiac motion. Experimental results and performance evaluation demonstrate the effectiveness of the proposed network. Apart from the efficacy of the proposed method, a visualization of the fetal cardiac motion using pseudo-color is provided. It is envisaged that the proposed method can be used in clinical applications requiring automatic detection of congenital fetal heart defects.
- S. Ge S, D. Maulik. Introduction: From fetal echocardiography to fetal cardiology: A journey of over half a century. Echocardiography. 2017, 34(12): 1757-1759.Google ScholarCross Ref
- Y Gao, Clinical application and prospect of fetal cardiac ultrasound . Western medicine, 2012, 024(4): 629-632. (in Chinese)Google Scholar
- S. K. Zhou, J. H. Park, B. Georgescu, , Image-Based Multiclass Boosting and Echocardiographic View Classification. In: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. 2006 (2): 1559–1565.Google Scholar
- V. K. Sudarshan, E.Y.K Ng, U. R. Acharya, S. M. Chou, R. S. Tan and D. N. Ghista, Computer-aided diagnosis of Myocardial Infarction using ultrasound images with DWT, GLCM and HOS methods: A comparative study, Computers in Biology and Medicine, 2015, 62: 86-93.Google ScholarDigital Library
- G. Carneiro, B. Georgescu, S. Good, and D. Comaniciu, Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree. In: IEEE Transactions on Medical Imaging, 2008, 27(9): 1342-1355.Google ScholarCross Ref
- G. Carneiro, F. Amat, B. Georgescu, , Semantic-based indexing of fetal anatomies from 3-D ultrasound data using global/semi-local context and sequential sampling. In: Computer Vision and Pattern Recognition, 2008 IEEE Conference on. June (2008), pp. 1-8.Google ScholarCross Ref
- A. I. L. Namburete, B. Rahmatullah, and J. A. Noble, Nakagami-Based AdaBoost Learning Framework for Detection of Anatomical Landmarks in 2D Fetal Neurosonograms. In: Annals of the BMVA 2 (2013), pp. 1–16.Google Scholar
- D. Ni, X. Yang, C. Xin, , Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search. Ultrasound in Medicine and Biology, 2014, 40(11): 2728-2742.Google ScholarCross Ref
- Q. Duan, E. D. Angelini, S. L. Herz , Dynamic cardiac information from optical flow using four dimensional ultrasound. IEEE Engineering in Medicine & Biology Conference. IEEE, 2006.Google Scholar
- L. Wang, P. Clarysse, Z. Liu, A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images. Medical Image Analysis, 2019, 57.Google Scholar
- J. L. Barron, D. J. Fleet, S. S. Beauchemin , Performance of optical flow techniques. Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92. 1992 IEEE Computer Society Conference on. IEEE.Google Scholar
- A. Czirok, D. G. Isai, E. Kosa , Optical-flow based non-invasive analysis of cardiomyocyte contractility. 2017, 7(1):10404.Google ScholarCross Ref
- J. L. Barron, D. J. Fleet, S. S. Beauchemin, T. A. Burkitt, Performance of optical flow techniques. In Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Champaign (CVPR), IL, USA, 15–18 June 1992; pp. 236–242.Google Scholar
- N. Mayer, E. Ilg, P. Hausser , A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016.Google Scholar
- A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser , Flownet: Learning optical flow with convolutional networks. In: IEEE Int. Conference on Computer Vision (ICCV), 2015.Google ScholarDigital Library
- P. Fischer, A. Dosovitskiy, T. Brox, Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT. Computerence, 2014.Google Scholar
- Ilg E, Mayer N, Saikia T , FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017.Google ScholarCross Ref
- A. Geiger, P. Lenz, C. Stiller , Vision meets robotics: the KITTI dataset. The International Journal of Robotics Research, 2013, 32(11): 1231-1237.Google ScholarDigital Library
- M. Aubry, D. Maturana, A. Efros, B. Russell, and J. Sivic.Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models. In CVPR, 2014.Google ScholarDigital Library
Recommendations
Mortality Prediction Based on Echocardiographic Data and Machine Learning: CHF, CHD, Aneurism, ACS Cases
AbstractThis paper represents the research results of echocardiographic study for early prediction of mortality. The classification task is solved by analyzing the echocardiographic data from medical information system. Echocardiographic data of 90000 ...
Coronary Occlusion Detection with 4D Optical Flow Based Strain Estimation on 4D Ultrasound
FIMH '09: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the HeartReal-time three-dimensional echocardiography (RT3DE) offers an efficient way to obtain complete 3D images of the heart over an entire cardiac cycle in just a few seconds. The complex 3D wall motion and temporal information contained in these 4D data ...
Quantitative validation of optical flow based myocardial strain measures using sonomicrometry
ISBI'09: Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to MacroDynamic cardiac metrics, including myocardial strains and displacements, provide a quantitative approach to evaluate cardiac function. However, in current clinical diagnosis, largely 2D strain measures are used despite that cardiac motions are complex ...
Comments