Deep Learning-Based Detection and Segmentation for BVS Struts in IVOCT Images

Bioresorbable Vascular Scaffold (BVS) is the latest stent type for the treatment of coronary artery disease. A major challenge of BVS is that once it is malapposed during implantation, it may potentially increase the risks of late stent thrombosis. Therefore it is important to analyze struts malapposition during implantation. This paper presents an automatic method for BVS malapposition analysis in intravascular optical coherence tomography images. Struts are firstly detected by a detector trained through deep learning. Then, struts boundaries are segmented using dynamic programming. Based on the segmentation, apposed and malapposed struts are discriminated automatically. Experimental results show that the proposed method successfully detected 97.7% of 4029 BVS struts with 2.41% false positives. The average Dice coefficient between the segmented struts and ground truth was 0.809. It concludes that the proposed method is accurate and efficient for BVS struts detection and segmentation, and enables automatic malapposition analysis.