Deep Learning Based Bioresorbable Vascular Scaffolds Detection in IVOCT Images

Abstract

Bioresorbable Vascular Scaffolds (BVS) are currently one of the most frequently-used type of stent during percutaneous coronary intervention. It's very important to conduct struts malappostion analysis during operation. Currently, BVS malappostion analysis in intravascular optical coherence tomography (IVOCT) images is mainly conducted manually, which is labor intensive and time consuming. In our previous work, a novel framework was presented to automatically detect and segment BVS struts for malappostion analysis. However, limited by the detection performance, the framework faced some challenges under complex background. In this paper, we proposed a robust BVS struts detection method based on Region-based Fully Convolutional Network (R-FCN). The detection model mainly consisted of two modules: 1) a Region Proposal Network (RPN), used to extract struts region of interest (ROIs) in the image and, 2) a detection module, used to classify the ROIs and regress a bounding box for each ROI. The network was initialized by pre-trained ImageNet model and then trained based on our labeled data which contained 1231 IVOCT images. Tested on a total of 480 IVOCT images with 4096 BVS struts, our method achieved 97.9% true positive rate with 4.79% false positive rate. It concludes that the proposed method is efficient and robust for BVS struts detection.