Deep-learning-based estimation of left ventricle myocardial strain from echocardiograms with occlusion artifacts
The results demonstrate improvements over previously reported performances while maintaining stability in echocardiograms with acoustic shadows. This methodology could be useful in clinical practice for the analysis of echocardiograms with noise artifacts and acoustic occlusions. Our code and trained models are publicly available at https://github.com/ArBioIIMAS/echo-gma.
Authors
- Nidiyare Hevia‐Montiel (ORCID: https://orcid.org/0000-0003-4238-7496)
- Alan Romero–Pacheco
- Jorge Pérez-González (ORCID: https://orcid.org/0000-0002-4069-4268)
- Fernando Arámbula Cosı́o (ORCID: https://orcid.org/0000-0001-7607-7686)
- Blanca Vázquez (ORCID: https://orcid.org/0000-0001-7708-8424)
Institutions
- Universidad Nacional Autónoma de México (MX)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-09-27
- DOI
- https://doi.org/10.1117/1.jmi.12.5.054002
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 0.00