Realtime automatic detection of heart failure in echocardiography
Abstract
Assessment of heart function by echocardiography is challenging for non-experts. In a patient with dyspnea, assessment of mitral annular excursion (MAE) and velocities are important for the diagnosis of heart failure. By automatic measurement of these parameters, a preliminary diagnosis could be performed by the non-expert. We propose an automatic algorithm to localize the mitral annular points in the apical tissue Doppler image, and estimate MAE, as well as the three peak velocities early systolic, early diastolic and late diastolic. Results on estimation accuracy and classification of systolic function based on the estimated mitral annular excursion are presented.
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A semi-automated algorithm based on cross correlation to track the tricuspid annulus points has been proposed to obtain TAPSE.
“A semi-automated algorithm has been proposed [9], based on cross correlation to track the tricuspid annulus (TA) points to obtain the TAPSE.”
From Convolutional Neural Networks for Automated Fetal Cardiac Assessment using 4D B-Mode Ultrasound (2019) · cites this paper as [9]
Cites
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Self-citationElnoamany, M. F., & Abdelhameed, A. M. (2005) · European Journal of Echocardiography · doi:10.1016/j.euje.2005.05.005 · Cited by 96 · see citing works
Real-time 3D segmentation of the left ventricle using deformable subdivision surfaces
Orderud, F., & Rabben, S. I. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587442 · Cited by 68 · see citing works
The relation between mitral annulus motion and ejection fraction: A nonlinear function
Emilsson, K., Alam, M., & Wandt, B. (2000) · Journal of the American Society of Echocardiography · doi:10.1067/mje.2000.107253 · Cited by 53 · see citing works
Okada, K., Mikami, T., Kaga, S., Onozuka, H., Inoue, M., Yokoyama, S., Nishino, H., Nishida, M., Matsuno, K., Iwano, H., et al. (2011) · European Journal of Echocardiography · doi:10.1093/ejechocard/jer154 · Cited by 30 · see citing works
Fast Automatic Measurement of Mitral Annulus Excursion Using a Pocket-Sized Ultrasound System
Snare, S. R., Mjølstad, O. C., Orderud, F., Haugen, B. O., & Torp, H. G. (2011) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2010.12.012 · Cited by 14 · see citing works
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Realtime Automatic Assessment of Cardiac Function in Echocardiography
Storve, S., Grue, J. F., Samstad, S., Dalen, H., Haugen, B. O., & Torp, H. (2016) · IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control · doi:10.1109/tuffc.2016.2518306 · Cited by 23 · see citing works
Self-citationConvolutional Neural Networks for Automated Fetal Cardiac Assessment using 4D B-Mode Ultrasound
Philip, M. E., Sowmya, A., Avnet, H., Ferreira, A., Stevenson, G., & Welsh, A. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) · doi:10.1109/isbi.2019.8759377 · Cited by 18 · see citing works
The Analysis of Heart Rate Fragmentation for Congestive Heart Failure
Wang, Y. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1213/2/022027 · Cited by 2 · see citing works
- Authors
- Storve, S., Grue, J. F., Samstad, S., Dalen, H., Haugen, B. O., & Torp, H.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Venue
- 2014 IEEE International Ultrasonics Symposium
- Publisher
- IEEE
- Published
- 2014-09
- Pages
- 1069-1072
- ISSN
- 1948-5719 (ISSN-L)
- OpenAlex
- W1975193081
- Cited by
- 3
- References
- 10
- Citations by year
- 2019: 2 · 2016: 1
- FWCI
- 1.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Ultrasonics Symposium (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- heart failure, tissue Doppler imaging, mitral annular velocity, automated measurement, dyspnea
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