School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China.
Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China.
J Biophotonics. 2021 Oct;14(10):e202100124. doi: 10.1002/jbio.202100124. Epub 2021 Jul 5.
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.
我们提出了一种基于血管壁 A 线特征的血管内腔自动分割方法，该方法利用血管内腔的连通区域在 A 线上具有唯一性的特点，检测出多个连通区域所共有的 A 线，通过遍历比较连通区域的位置、共享比例和面积比，识别出由血液伪影产生的连通区域，从而有效地去除血液伪影对内腔分割的影响。我们利用 1076 张临床 IVOCT 图像中选取的 216 张具有严重血液伪影的挑战性图像对这三种方法进行了比较。评估了所提出方法的度量标准，包括 Dice 指数、Jaccard 指数和准确性，分别为 94.57%、90.12%和 98.02%。与基于先前形态特征方法和广泛使用的动态规划方法的自动内腔分割方法相比，所提出方法的度量标准有了显著提高，尤其是在具有严重血液伪影的挑战性图像中。
