Automatic segmentation of vessel lumen in intravascular optical coherence tomography images

After decades of study, coronary artery disease (CAD) remains the most common cause of death worldwide. The vessel lumen is used to analyze the degree of vessel blockage, stent coverage and other important features. By providing high resolution description of coronary morphology, intravascular optical coherence tomography (IVOCT) has been increasingly used for CAD research, diagnosis and treatment evaluation. This paper presents an automated method to segment the vessel lumen in IVOCT images for precise quantification analysis. 25 clinical IVOCT image pullback runs were used for the validation. All the lumen contours were successfully segmented and the intraclass correlation coefficient of the results from the presented method and the manually segmented results by expert was 0.974. The validation result indicates that the presented approach may be able to contribute to the accurate and robust quantification analysis in IVOCT images.