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Automatic segmentation of ultrasound images of carotid atherosclerotic plaque based on Dense-UNet
Chengliang Deng1, Jianhua Adu2, Shenghua Xie3,4
1School of Software Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China.
Insights
A new Dense-UNet network effectively automates carotid plaque segmentation from ultrasound images, improving plaque stability assessment and reducing manual effort for clinicians.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Cardiovascular disease research
Background:
- Carotid atherosclerosis plaque rupture is a major cause of myocardial infarction and stroke.
- Accurate segmentation of carotid plaque ultrasound images is crucial for assessing plaque stability.
- Current manual segmentation methods are time-consuming and rely on practitioner experience.
Purpose of the Study:
- To develop an automated intelligent segmentation method for carotid plaque ultrasound images.
- To improve the efficiency and accuracy of carotid plaque analysis.
Main Methods:
- Combined U-Net and DenseNet networks for automated segmentation.
- Compared Dense-UNet with traditional U-Net and ResUNet networks.
- Evaluated performance using Dice and VOE coefficients.
Main Results:
- Dense-UNet demonstrated superior performance in automated segmentation compared to U-Net and ResUNet.
- The proposed method achieved optimal results in segmenting carotid atherosclerotic plaque ultrasound images.
Conclusions:
- The Dense-UNet network enables automatic segmentation of atherosclerotic plaque ultrasound images.
- This automated approach can assist clinicians in plaque evaluation and stability assessment.
Background:
Carotid atherosclerosis plaque rupture is an important cause of myocardial infarction and stroke. The effective segmentation of ultrasound images of carotid atherosclerotic plaques aids clinicians to accurately assess plaque stability. At present, this procedure relies mainly on the experience of the medical practitioner to manually segment the ultrasound image of the carotid atherosclerotic plaque. This method is also time-consuming.
Objective:
This study intends to establish an automatic intelligent segmentation method of ultrasound images of carotid plaque.
Methods:
The present study combined the U-Net and DenseNet networks, to automatically segment the ultrasound images of carotid atherosclerotic plaques. The same test set was selected and segmented using the traditional U-Net network and the ResUNet network. The prediction results of the three network models were compared using Dice (Dice similarity coefficient), and VOE (volumetric overlap error) coefficients.
Results:
Compared with the existing U-Net network and ResUNet network, the Dense-UNet network exhibited an optimal effect on the automated segmentation of the ultrasound images.
Conclusion:
The Dense-UNet network could realize the automatic segmentation of atherosclerotic plaque ultrasound images, and it could assist medical practitioners in plaque evaluation.
