An impoved Adaboost algorithm, together with a profile segmentation method based on dynamic programming (DP), is proposed for automatic detection and segmentation of bioresorbable vascular scaffold (BVS) in intravascular optical coherence tomography (IVOCT) imaging system, to achieve auto estimation on the strut malapposition. During detection, the multi-layer decision tree is applied to the construction of Adaboost classifier, in order to detect the position and size of each strut. Then, the DP algoritm is adopted to segment the struts’ boundaries based on detection results. Finally, combined with the segmentation results, struts malapposition is caculated. Experimental results show that our method reaches the detection recall rate of 91.6% with the precision of 87.3%, and the average Dice coefficient of segmentation is 0.80. It suggests that our method can accurately achieve the detection and the segmentation of BVS struts in IVOCT images, and has high robustness.