Bioresorbable Vascular scaffold (BVS) is a promising type of stent in percutaneous coronary intervention. Struts apposition assessment is important to ensure the safety of implanted BVS. Currently, BVS struts apposition analysis in 2D IVOCT images still depends on manual delineation of struts, which is labor intensive and time consuming. Automatic struts segmentation is highly desired to simplify and speed up quantitative analysis. However, it is difficult to segment struts accurately based on the contour, due to the influence of fractures inside strut and blood artifacts around strut. In this paper, a novel framework of automatic struts segmentation based on four corners is introduced, in which prior knowledge is utilized that struts have obvious feature of box-shape. Firstly, a cascaded AdaBoost classifier based on enriched haar-like features is trained to detect struts corners. Then, segmentation result can be obtained based on the four detected corners of each strut. Tested on the same five pullbacks consisting of 480 images with strut, our novel method achieved an average Dice’s coefficient of 0.85 for strut segmentation areas, which is increased by about 0.01 compared to the state-of-the-art. It concludes that our method can segment struts accurately and robustly and has better performance than the state-of-the-art. Furthermore, automatic struts malapposition analysis in clinical practice is feasible based on the segmentation results. © 2018, Springer Nature Switzerland AG.