Fully Automated Lumen Segmentation Method for Intracoronary Optical Coherence Tomography.

Area of Science:

• Cardiovascular Imaging
• Interventional Cardiology
• Medical Image Analysis

Background:

• Optical Coherence Tomography (OCT) provides high-resolution intracoronary images crucial for diagnosing coronary artery disease.
• Accurate lumen detection in OCT images is vital for patient prognosis and treatment planning in interventional cardiology.
• Current OCT image analysis faces challenges in precise lumen segmentation.

Purpose of the Study:

• To develop an efficient, automated algorithm for lumen segmentation in OCT images.
• To address the need for precise lumen detection to improve coronary artery disease analysis.
• To provide a tool for automated quantitative lumen analysis.

Main Methods:

• A novel approach for lumen segmentation in OCT images was designed.
• The algorithm includes preprocessing steps for artifact removal (speckle noise, circular rings, guide wire).
• Image data was converted between polar and Cartesian coordinates, followed by a segmentation algorithm.

Main Results:

• The algorithm was tested on 667 OCT frames.
• Lumen border extraction showed high correlation with ground truth.
• An intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.97-0.98) was achieved.

Conclusions:

• The proposed algorithm enables fully automated lumen segmentation in OCT images.
• This automated tool can be utilized for quantitative lumen analysis.
• The method enhances the precision of OCT image analysis for cardiovascular applications.