ArticleIEEE transactions on medical imaging2021
Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Coordinate-Independent 3-D Ultrasound Principal Stretch and Direction Imaging.IEEE transactions on bio-medical engineering · 2024Article
- A preoperative planning procedure of septal myectomy for hypertrophic obstructive cardiomyopathy using image-based computational fluid dynamics simulations and shape optimization.Scientific reports · 2024Article
- Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography.IEEE transactions on medical imaging · 2024Article
- Model-based image updating in deep brain stimulation with assimilation of deep brain sparse data.Medical physics · 2023Article
- Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography.Medical image analysis · 2023Article
- Novel Cardiac Computed Tomography Methods for the Assessment of Anthracycline Induced Cardiotoxicity.Frontiers in cardiovascular medicine · 2022Review
- Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation andIEEE open journal of ultrasonics, ferroelectrics, and frequency control · 2021Article
- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking and Segmentation in 4D Echocardiography.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- A SEMI-SUPERVISED JOINT LEARNING APPROACH TO LEFT VENTRICULAR SEGMENTATION AND MOTION TRACKING IN ECHOCARDIOGRAPHY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
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Authors and funding
12 authors.
Funding
Abstract
Reliable motion estimation and strain analysis using 3D+ time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose a semi-supervised Multi-Layered Perceptron (MLP) network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation. We validated the semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarct regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts.
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