Automatic Side Branch Ostium Detection and Main Vascular Segmentation in Intravascular Optical Coherence Tomography Images

Intravascular optical coherence tomography is the state-of-the-art imaging modality in percutaneous coronary intervention planning and evaluation, in which side branch ostium and main vascular measurements play critical roles. However, manual measurement is time consuming and labor intensive. In this paper, we propose a fully automatic method for side branch ostium detection and main vascular segmentation to make up manual deficiency. In our method, side branch ostium points are first detected and subsequently used to divide the lumen contour into side branch and main vascular regions. Based on the division, main vascular contour is then smoothly fitted for segmentation. In side branch ostium detection, our algorithm creatively converts the definition of curvature into the calculation of the signed included angles in global view, and originally applies a differential filter to highlight the feature of side branch ostium points. A total of 4618 images from 22 pullback runs were used to evaluate the performance of the presented method. The validation results of side branch detection were TPR = 82.8<inline-formula> <tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, TNR = 98.7<inline-formula><tex-math notation="LaTeX">$\%$ </tex-math></inline-formula>, PPV = 86.8<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, NPV = 98.7<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. The average ostial distance error (ODE) was 0.22 mm, and the DSC of main vascular segmentation was 0.96. In conclusion, the qualitative and quantitative evaluation indicated that the presented method is effective and accurate.