ArticleMedical image analysis2023
Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- PET Head Motion Estimation Using Supervised Deep Learning With Attention.IEEE transactions on medical imaging · 2026Article
- Speckle tracking technology and investigation of risk factors for premature ventricular contraction-induced cardiomyopathy.Frontiers in cardiovascular medicine · 2025Article
- Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.Journal of imaging informatics in medicine · 2024Article
- Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography.IEEE transactions on medical imaging · 2024Article
- Ultrasound Image Analysis with Vision Transformers-Review.Diagnostics (Basel, Switzerland) · 2024Review
- Cross-Attention for Improved Motion Correction in Brain PET.Machine learning in clinical neuroimaging : 6th international workshop, MLCN 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MLCN (Workshop) (6th : 2023 : Vancouver, B.C.) · 2023Article
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Authors and funding
9 authors at 1 institution in 1 country.
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Abstract
Myocardial ischemia/infarction causes wall-motion abnormalities in the left ventricle. Therefore, reliable motion estimation and strain analysis using 3D+time echocardiography for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. Previous unsupervised cardiac motion tracking methods rely on heavily-weighted regularization functions to smooth out the noisy displacement fields in echocardiography. In this work, we present a Co-Attention Spatial Transformer Network (STN) for improved motion tracking and strain analysis in 3D echocardiography. Co-Attention STN aims to extract inter-frame dependent features between frames to improve the motion tracking in otherwise noisy 3D echocardiography images. We also propose a novel temporal constraint to further regularize the motion field to produce smooth and realistic cardiac displacement paths over time without prior assumptions on cardiac motion. Our experimental results on both synthetic and in vivo 3D echocardiography datasets demonstrate that our Co-Attention STN provides superior performance compared to existing methods. Strain analysis from Co-Attention STNs also correspond well with the matched SPECT perfusion maps, demonstrating the clinical utility for using 3D echocardiography for infarct localization.
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