Related Experiment Video
Updated: Jun 6, 2025
Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
Published on: October 20, 2016
U-shape-based network for left ventricular segmentation in echocardiograms with contrastive pretraining
Zhengkun Qian1, Tao Hu2, Jianming Wang3
1School of Mathematics and Computer Science, Dali University, Dali, China.
Insights
This study presents an efficient deep learning model for segmenting the left ventricle in echocardiograms, achieving high accuracy with reduced computational cost for clinical applications.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiovascular diseases are a leading cause of death, necessitating accurate cardiac function assessment.
- Manual delineation of the left ventricle on echocardiograms is time-consuming and subjective.
- Current deep learning models often prioritize accuracy over computational efficiency for clinical use.
Purpose of the Study:
- To develop a computationally efficient deep learning model for left ventricle segmentation in echocardiograms.
- To reduce the computational complexity and parameter count of existing segmentation models.
- To improve the performance of left ventricle segmentation while maintaining low resource requirements.
Main Methods:
- Proposed a novel model combining SwiftFormer Encoder and U-Lite Decoder.
- Incorporated Spatial and Channel reconstruction Convolution (SCConv) module.
- Replaced Binary Cross Entropy Loss (BCELoss) with Polynomial Loss (PolyLoss).
Main Results:
- Achieved a Dice similarity coefficient of 0.92714 for left ventricle segmentation on the EchoNet-Dynamic dataset.
- Reported low computational complexity with 4472.55 M FLOPs and 28.96 M Parameters.
- Demonstrated competitive segmentation performance at a significantly reduced computational cost.
Conclusions:
- The proposed model offers an efficient and accurate solution for left ventricle segmentation in echocardiography.
- The integration of SCConv and PolyLoss enhances segmentation performance.
- This approach addresses the need for high-performance computing in clinical cardiac imaging applications.
Abstract:
Cardiovascular diseases, characterized by high morbidity, disability, and mortality rates, are a collective term for disorders affecting the heart's structure or function. In clinical practice, physicians often manually delineate the left ventricular border on echocardiograms to obtain critical physiological parameters such as left ventricular volume and ejection fraction, which are essential for accurate cardiac function assessment. However, most state-of-the-art models focus excessively on pushing the boundaries of segmentation accuracy at the expense of computational complexity, overlooking the substantial demand for high-performance computing resources required for model inference in clinical applications. This paper introduces a novel left ventricle echocardiographic segmentation model that efficiently combines the SwiftFormer Encoder and U-Lite Decoder to reduce network parameter count and computational complexity. Additionally, we incorporate the Spatial and Channel reconstruction Convolution (SCConv) module through spatial and channel reconstruction during downsampling and replace the Binary Cross Entropy Loss (BCELoss) with Polynomial Loss (PolyLoss) to achieve superior segmentation performance. On the EchoNet-Dynamic dataset, our network achieves a Dice similarity coefficient of 0.92714 for left ventricle segmentation, with FLoating-point Operations Per Second (FLOPs) and Parameters of just 4472.55 M and 28.96 M respectively. Extensive experimental results on the EchoNet-Dynamic dataset demonstrate that the proposed modifications deliver competitive performance at a lower computational cost.
