Robust Segmentation of Intima-Media Borders With Different Morphologies and Dynamics During the Cardiac Cycle

Area of Science:

• Medical imaging
• Ultrasound technology
• Biomedical engineering

Background:

• Carotid intima-media (IM) border segmentation in ultrasound is difficult due to noise and variable border characteristics.
• Accurate segmentation is crucial for assessing cardiovascular health and disease progression.

Purpose of the Study:

• To develop and evaluate a robust state-space framework for sequential carotid IM border segmentation.
• To address challenges posed by image noise, varying IM morphologies, and dynamics in ultrasound sequences.

Main Methods:

• A state-space framework utilizing an H-infinity filter was developed.
• A grayscale-derivative constraint snake was employed for accurate measurements within the framework.
• The method was validated against manual tracings on synthetic models and real ultrasound data from 156 subjects.

Main Results:

• The framework achieved small segmentation errors: lumen intima (LI) at 53 ± 67 μm and media-adventitia (MA) at 57 ± 63 μm.
• Results showed excellent agreement with manual segmentation (LI bias = 1.44 μm; MA bias = -3.38 μm).
• The approach demonstrated robustness across diverse IM border morphologies, dynamics, and noise levels.

Conclusions:

• The developed state-space framework provides accurate and robust segmentation of carotid IM borders in ultrasound sequences.
• This method holds significant potential for improving the accuracy and reliability of carotid ultrasound analysis for clinical diagnosis.
• The framework's ability to handle various image conditions makes it suitable for widespread clinical application.