Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography. Issue 10 (5th July 2021)

• Record Type: Journal Article
• Authors: Zhu, Fengyu; Ding, Zhenyang; Tao, Kuiyuan; Li, Qingrui; Kuang, Hao; Tian, Feng; Zhou, Shanshan; Hua, Peidong; Hu, Jingqi; Shang, Mingjian; Yu, Yin; Liu, Tiegen
• Is Part Of: Journal of biophotonics. Volume 14:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-05
• Subjects: biomedical imaging -- intravascular imaging -- lumen segmentation -- optical coherence tomography; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202100124
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: We present an automatic lumen segmentation method using uniqueness of connected region for intravascular optical coherence tomography (IVOCT), which can effectively remove the effect on lumen segmentation caused by blood artifacts. Utilizing the uniqueness of vascular wall on A‐lines, we detect the A‐lines shared by multiple connected regions, identify connected regions generated by blood artifacts using traversal comparison of connected regions' location, shared ratio and area ratio and then remove all artifacts. We compare these three methods by 216 challenging images with severe blood artifacts selected from clinical 1076 IVOCT images. The metrics of the proposed method are evaluated including Dice index, Jaccard index and accuracy of 94.57%, 90.12%, 98.02%. Compared with automatic lumen segmentation based on the previous morphological feature method and widely used dynamic programming method, the metrics of the proposed method are significantly enhanced, especially in challenging images with severe blood artifacts. Abstract : We present an automatic lumen segmentation method using uniqueness of connected region for intravascular optical coherence tomography (IVOCT), which can effectively remove the effect on lumen segmentation caused by blood artifacts. Utilizing the uniqueness of vascular wall on A‐lines, we detect the A‐lines shared by multiple connected regions, identify connected regions generated by blood artifacts using traversal comparison of connected regions' location, shared ratio and area ratio and then remove all artifacts.