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Updated: Jul 14, 2025
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography
Ziyu Guo1, Yuting Zhang2, Zishan Qiu3
1College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
Insights
This study introduces a semi-supervised method using contrastive learning for segmenting cardiac structures in echocardiography, improving cardiovascular disease diagnosis accuracy with less labeled data.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Cardiac diseases pose a significant mortality risk.
- Echocardiography is a vital, non-invasive diagnostic tool.
- Accurate segmentation of cardiac structures is essential but challenging due to image quality and anatomical variations.
Purpose of the Study:
- To develop a semi-supervised method for precise cardiac structure segmentation in echocardiographic images.
- To address challenges like low contrast, incomplete structures, and unclear borders.
- To leverage unlabeled data for improved segmentation performance.
Main Methods:
- Applied a contrastive learning strategy.
- Developed a semi-supervised learning approach for echocardiographic image segmentation.
- Evaluated the method on the public CAMUS dataset.
Main Results:
- Achieved high Dice Similarity Coefficients (DSC) on two-chamber (2CH) and four-chamber (4CH) images, even with limited labeled data (e.g., 0.916 and 0.928 with full labels).
- Demonstrated superior performance and fewer parameters compared to existing methods.
- Effectively improved segmentation accuracy despite image quality challenges.
Conclusions:
- The proposed semi-supervised method enhances the accuracy of cardiac structure segmentation in echocardiography.
- This approach effectively utilizes unlabeled data, aiding in more accurate cardiovascular disease (CVD) diagnosis and screening.
- The method offers a promising solution for improving echocardiographic analysis.
Abstract:
Cardiac diseases have high mortality rates and are a significant threat to human health. Echocardiography is a commonly used imaging technique to diagnose cardiac diseases because of its portability, non-invasiveness and low cost. Precise segmentation of basic cardiac structures is crucial for cardiologists to efficiently diagnose cardiac diseases, but this task is challenging due to several reasons, such as: (1) low image contrast, (2) incomplete structures of cardiac, and (3) unclear border between the ventricle and the atrium in some echocardiographic images. In this paper, we applied contrastive learning strategy and proposed a semi-supervised method for echocardiographic images segmentation. This proposed method solved the above challenges effectively and made use of unlabeled data to achieve a great performance, which could help doctors improve the accuracy of CVD diagnosis and screening. We evaluated this method on a public dataset (CAMUS), achieving mean Dice Similarity Coefficient (DSC) of 0.898, 0.911, 0.916 with 1/4, 1/2 and full labeled data on two-chamber (2CH) echocardiography images, and of 0.903, 0.921, 0.928 with 1/4, 1/2 and full labeled data on four-chamber (4CH) echocardiography images. Compared with other existing methods, the proposed method had fewer parameters and better performance. The code and models are available at https://github.com/gpgzy/CL-Cardiac-segmentation.
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