# Automated Lumen Segmentation in Carotid Artery Ultrasound Images Based on Adaptive Generated Shape Prior

## TL;DR

This paper introduces a new method for accurately segmenting carotid artery lumens in ultrasound images, even when image quality is poor.

## Contribution

A novel shape-prior-based segmentation algorithm that adapts to vessel growth trends for improved accuracy in low-quality images.

## Key findings

- The proposed method achieved an average Dice coefficient of 92.38% on 247 carotid artery images.
- It outperformed existing mathematical models in lumen segmentation accuracy.
- The method is effective for low-quality images, aiding in cardiovascular and cerebrovascular disease diagnosis.

## Abstract

Ultrasound imaging is vital for diagnosing carotid artery vascular lesions, highlighting the importance of accurately segmenting lumens in ultrasound images to prevent, diagnose and treat vascular diseases. However, noise artifacts, blood residue and discontinuous lumens significantly affect segmentation accuracy. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm which is guided by an adaptively generated shape prior. To tackle the above challenges, we introduce a shape-prior-based segmentation method for carotid artery lumen walls. The shape prior in this study is adaptively generated based on the evolutionary trend of vessel growth. Shape priors guide and constrain the active contour, resulting in precise segmentation. The efficacy of the proposed model was confirmed using 247 carotid artery ultrasound images, with experimental results showing an average Dice coefficient of 92.38%, demonstrating superior segmentation performance compared to existing mathematical models. Our method can quickly and effectively perform accurate lumen segmentation on low-quality carotid artery ultrasound images, which is of great significance for the diagnosis of cardiovascular and cerebrovascular diseases.