Adaboost-based detection and segmentation of bioresorbable vascular scaffolds struts in IVOCT images

Bioresorbable Vascular Scaffolds (BVS) are the most promising type of stent in percutaneous coronary intervention. For accurate BVS struts apposition assessment, intravascular optical coherence tomography (IVOCT) is the state-of-the-art imaging modality. However, manual analysis for IVOCT frames is time consuming and labor intensive. In this paper, we propose an automatic method for BVS struts center and region detection based on Adaboost algorithm and Haar-like features. Then, dynamic programming algorithm is applied to segment the contour of BVS struts. Based on the segmentation results, the apposed or malapposed struts can be automatically distinguished. By comparing the manual and automatic detection and segmentation results, our method correctly detected and segmented 87.7% of 4029 BVS struts with 18.6% false positives. The average Dice's coefficient for the correctly detected struts was 0.78. In conclusion, the evaluation suggested that this method is accurate and robust for BVS struts detection and segmentation.