Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random...

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Cardiovascular Research

Background:

• Intravascular imaging like Optical Coherence Tomography (OCT) aids interventional cardiology by visualizing arterial lumen.
• High-resolution OCT images reveal atherosclerosis but speckle noise hinders analysis of subtle lumen variations, plaque vulnerability, and necrosis.
• Accurate lumen segmentation is crucial for assessing plaque characteristics and predicting acute coronary events.

Purpose of the Study:

• To develop and validate a robust lumen segmentation method for OCT imaging.
• To overcome limitations of speckle noise in OCT analysis for improved plaque characterization.
• To enhance the investigation of lumen topology, plaque vulnerability, and necrotic areas in coronary arteries.

Main Methods:

• A novel lumen segmentation approach utilizing OCT imaging physics-based graph representation and random walks.
• Graph edge weights incorporate OCT signal attenuation physics models.
• Seed initialization for random walks uses optical backscattering maxima tracking and global grayscale statistics.

Main Results:

• Achieved high lumen versus tunica segmentation accuracy (Cohen's kappa: 0.9786 ±0.0061) compared to cardiologist annotations.
• Demonstrated consistent segmentation across 15 in vitro and 6 in vivo OCT datasets (150-200 frames each).
• Quantified segmentation accuracy using Kullback-Leibler (5.17 ±2.39) and Bhattacharya (0.56 ±0.28) divergence measures.

Conclusions:

• The proposed method reliably segments arterial lumen in OCT images, even with vulnerability cues and necrotic pools.
• High accuracy and consistency confirm the method's effectiveness for clinical application in interventional cardiology.
• This framework advances OCT image analysis by integrating tissue physics for accurate segmentation and classification.