Abstract
Doppler echocardiography is a widely applied modality for the functional assessment of heart valves, such as the mitral valve. Currently, Doppler echocardiography analysis is manually performed by human experts. This process is not only expensive and time-consuming, but often suffers from intra- and inter-observer variability. An automated analysis tool for non-invasive evaluation of cardiac hemodynamic has potential to improve accuracy, patient outcomes, and save valuable resources for health services. Here, a robust algorithm is presented for automatic Doppler Mitral Inflow peak velocity detection utilising state-of-the-art deep learning techniques. The proposed framework consists of a multi-stage convolutional neural network which can process Doppler images spanning arbitrary number of heartbeats, independent from the electrocardiogram signal and any human intervention. Automated measurements are compared to Ground-truth annotations obtained manually by human experts. Results show the proposed model can efficiently detect peak mitral inflow velocity achieving an average F1 score of 0.88 for both E- and A-peaks across the entire test set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Corriveau, M.M., Johnston, K.W.: Interobserver variability of carotid doppler peak velocity measurements among technologists in an ICAVL-accredited vascular laboratory. J. Vasc. Surg. 39(4), 735–741 (2004)
Elwazir, M.Y., Akkus, Z., Oguz, D., Ye, Z., Oh, J.K.: Fully automated mitral inflow doppler analysis using deep learning. In: 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 691–696. IEEE (2020)
Jahren, T.S., Steen, E.N., Aase, S.A., Solberg, A.H.S.: Estimation of end-diastole in cardiac spectral doppler using deep learning. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67(12), 2605–2614 (2020)
Lane, E.S., et al.: Automated multi-beat tissue doppler echocardiography analysis using deep neural networks. Med. Biol. Eng. Comput., 1–16 (2023)
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Park, J.H., Zhou, S.K., Jackson, J., Comaniciu, D.: Automatic mitral valve inflow measurements from doppler echocardiography. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 983–990. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_117
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Stern, A., et al.: Heatmap-based 2D landmark detection with a varying number of landmarks. In: Bildverarbeitung für die Medizin 2021. I, pp. 22–27. Springer, Wiesbaden (2021). https://doi.org/10.1007/978-3-658-33198-6_7
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724–4732 (2016)
Zamzmi, G., Hsu, L.Y., Li, W., Sachdev, V., Antani, S.: Fully automated spectral envelope and peak velocity detection from doppler echocardiography images. In: Medical Imaging 2020: Computer-Aided Diagnosis, vol. 11314, pp. 1053–1064. SPIE (2020)
Acknowledgement
This work was supported in part by the British Heart Foundation, UK (Grant no. RG/F/22/110059). J Jevsikov is supported by the Vice Chancellor’s Scholarship at the University of West London. We are grateful to the following experts for their invaluable input in labelling images: Arjun Ghosh, Maysaa Zetani, Mahmoud Tawil, Luxy Ananthan, Camelia Demetrescu, Amar Singh, Sanjeev Bhattacharyya, Joban Sehmi, Kavitha Vimalesvaran, Abdallah Al-Mohammad, Bushra Rana, Tiffany Ng.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jevsikov, J. et al. (2023). Automated Analysis of Mitral Inflow Doppler Using Deep Neural Networks. In: Bernard, O., Clarysse, P., Duchateau, N., Ohayon, J., Viallon, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science, vol 13958. Springer, Cham. https://doi.org/10.1007/978-3-031-35302-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-35302-4_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35301-7
Online ISBN: 978-3-031-35302-4
eBook Packages: Computer ScienceComputer Science (R0)