Automatic bifurcation detection utilizing pullback characteristics of bifurcation in intravascular optical coherence tomography

Abstract

Bifurcation detection in coronary arteries is significant since it influences the treatment strategy selection and optimization. Bifurcations are also reliable landmarks for image registration. Intravascular optical coherence tomography (IVOCT) is a high-resolution imaging modality that is very useful in percutaneous coronary intervention stenting optimization. We present a bifurcation identification method utilizing pullback characteristics for IVOCT, which can effectively identify the bifurcations with a small size. The longitudinal view of the pullback will appear as an outward discontinuity in the bifurcation area. By detecting this discontinuity, bifurcation can be identified with high accuracy. We also use the normal vectors method to extract the ostium of bifurcation. We compare the proposed method with the widely-used distance transformation method by clinical 5302 IVOCT images from 22 pullbacks. The average metrics of true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) for the proposed method are 86.97%, 98.50%, 85.56%, and 98.67%, respectively. TPR, PPV, and NPV by the proposed method are improved by 40.24%, 9.31%, 3.90%, and TNR is on par compared with the distance transformation method. Especially in the small bifurcation identification, TPR of the proposed method is 64.71% higher than the distance transformation method with a bifurcation area ratio less than 0.2.