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Updated: Oct 14, 2025
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography
Shawn S Ahn1, Kevinminh Ta1, Stephanie Thorn2
1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Insights
This study introduces a novel multi-frame attention network for segmenting the left ventricle in 3D echocardiography. The method significantly improves segmentation accuracy by utilizing spatiotemporal features from image sequences.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Deep Learning in Healthcare
Background:
- Echocardiography is vital for cardiovascular health assessment.
- Left ventricle segmentation is critical for quantifying ejection fraction.
- Accurate segmentation in 3D echocardiography is challenging.
Purpose of the Study:
- To develop an improved method for left ventricle segmentation in 3D echocardiography.
- To leverage multi-frame attention mechanisms for enhanced segmentation performance.
- To address the challenges of manual and semi-automated segmentation in cardiac imaging.
Main Methods:
- Proposed a novel multi-frame attention network for 3D echocardiography segmentation.
- The network utilizes spatiotemporal features from image sequences.
- Evaluated on 51 in vivo porcine 3D+time echocardiography datasets.
Main Results:
- The multi-frame attention network significantly improved left ventricle segmentation performance.
- Utilizing correlated spatiotemporal features enhanced segmentation accuracy.
- Outperformed standard deep learning-based medical image segmentation models.
Conclusions:
- The proposed multi-frame attention network offers a robust solution for left ventricle segmentation in 3D echocardiography.
- This approach enhances the accuracy of clinical measurements like ejection fraction.
- The method shows promise for improving automated cardiac image analysis.
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.
