Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography

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.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

“…Segmentation of the coronary artery lumen contour is perhaps the simplest task for automated techniques when there is no atherosclerotic disease and there has been appropriate clearance of blood from the OCT images. Here, globally used binarization methods [ 59 ], such as Otsu filtering [ 60 , 61 , 62 , 63 ], morphological operations, edge detection [ 64 , 65 , 66 ] and curve fitting [ 67 ] were often sufficient to automatically delineate the lumen. However, these methods are challenged when facing bifurcation regions and catheter artefacts, as well as improper blood clearance, which are not uncommon occurrences in clinical scenarios.…”

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

“…Segmentation of the coronary artery lumen contour is perhaps the simplest task for automated techniques when there is no atherosclerotic disease and there has been appropriate clearance of blood from the OCT images. Here, binarisation methods [56], such as Otsu filtering [57][58][59][60], morphological operations, edge detection [61][62][63] and curve fitting [64] were often sufficient to automatically delineate the lumen. However, these methods are challenged when facing bifurcation regions and catheter artefacts, as well as improper blood clearance, which are not uncommon occurrences in clinical scenarios.…”