Author/Authors :
Cao, Yihui University of Chinese Academy of Sciences - Beijing, China , Cheng, Kang Department of Cardiology - Xijing Hospital - Fourth Military Medical University - Xi’an - Shaanxi, China , Qin, Xianjing Department of Aerospace Biodynamics - Fourth Military Medical University - Xi’an - Shaanxi, China , Yin, Qinye School of the Electronic and Information Engineering - Xi’an Jiaotong University - Xi’an, China , Li, Jianan Xi’an Institute of Optics and Precision Mechanics - Chinese Academy of Sciences - Xi’an - Shaanxi, China , Zhu, Rui Xi’an Institute of Optics and Precision Mechanics - Chinese Academy of Sciences - Xi’an - Shaanxi, China , Zhao, Wei Xi’an Institute of Optics and Precision Mechanics - Chinese Academy of Sciences - Xi’an - Shaanxi, China
Abstract :
Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and
fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular
lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this
paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge.
Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the
leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our
proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the
experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients
and the average DSC value was 98.1% ± 1.1%.
Keywords :
Lumen , Tomography , Segmentation , IVOCT
