Key result
Deep learning models outperform traditional methods in echocardiographic image segmentation automation and accuracy.
Why the study?
A comprehensive overview of echocardiographic image segmentation techniques in cardiovascular diseases is needed to compare methods and address clinical application challenges.
Does the use of machine learning and deep learning algorithms improve the automation and accuracy of echocardiographic image segmentation in cardiovascular diseases compared to traditional processing methods?
Does the use of machine learning and deep learning algorithms improve the automation and accuracy of echocardiographic image segmentation in cardiovascular diseases compared to traditional processing methods?
Deep learning-based ultrasound segmentation significantly improves the accuracy and automation of echocardiographic analysis for cardiovascular diseases, though limited annotated datasets and generalizability remain barriers to clinical integration.
This review provides a comprehensive overview of echocardiographic image segmentation techniques in representative clinical contexts, including valvular heart disease (VHD), congenital heart disease (CHD), and left ventricular (LV) dysfunction and heart failure. The article first outlines the standardized workflow for ultrasound (US) image segmentation, establishing a technical framework for comparing and evaluating different approaches. This is followed by a brief overview of the pathophysiological characteristics of these conditions, highlighting their anatomical segmentation requirements and clinical implications. Subsequently, the review summarizes the evolution and key contributions of traditional image processing, machine learning (ML), and deep learning (DL) methods across different disease types. Comparative analysis indicates that DL models, with their end-to-end training strategies and hierarchical feature representation, demonstrate substantial advantages in segmentation automation and accuracy. Finally, the article discusses current challenges in data acquisition, model generalizability, and clinical integration, and explores future research directions to promote the development and translational application of intelligent echocardiographic analysis.
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Li et al. (2026) conducted a review in Cardiovascular Disease (Valvular heart disease, congenital heart disease, left ventricular dysfunction and heart failure). Deep learning ultrasound image segmentation techniques vs. Traditional image processing and machine learning methods was evaluated. Deep learning models demonstrate substantial advantages in echocardiographic image segmentation automation and accuracy compared to traditional and machine learning methods.
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