Automatic segmentation of ultrasound images of carotid atherosclerotic plaque based on Dense-UNet

Abstract

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.