ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 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.
- 4-D Reconstruction of Fetal Left Ventricle From Echocardiography via 2.5-D Radial Segmentation and Graph-Fourier Reconstruction.IEEE transactions on medical imaging · 2026Article
- Temporal Features-Fused Vision Retentive Network for Echocardiography Image Segmentation.Sensors (Basel, Switzerland) · 2025Article
- EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning.PeerJ. Computer science · 2025Article
- FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system.Heliyon · 2024Article
- Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography.IEEE transactions on medical imaging · 2024Article
- Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography.Medical image analysis · 2023Article
- Advanced Ultrasound and Photoacoustic Imaging in Cardiology.Sensors (Basel, Switzerland) · 2021Review
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Authors and funding
6 authors.
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Abstract
Echocardiography is one of the main imaging modalities used to assess the cardiovascular health of patients. Among the many analyses performed on echocardiography, segmentation of left ventricle is crucial to quantify the clinical measurements like ejection fraction. However, segmentation of left ventricle in 3D echocardiography remains a challenging and tedious task. In this paper, we propose a multi-frame attention network to improve the performance of segmentation of left ventricle in 3D echocardiography. The multi-frame attention mechanism allows highly correlated spatiotemporal features in a sequence of images that come after a target image to be used to augment the performance of segmentation. Experimental results shown on 51 in vivo porcine 3D+time echocardiography images show that utilizing correlated spatiotemporal features significantly improves the performance of left ventricle segmentation when compared to other standard deep learning-based medical image segmentation models.
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Registered trials
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