A robust automated method to detect stent struts in 3D intravascular optical coherence tomographic image sequences

Intravascular optical coherence tomography (IVOCT) provides very high resolution cross-sectional image sequences of vessels. It has been rapidly accepted for stent implantation and its follow up evaluation. Given the large amount of stent struts in a single image sequence, only automated detection methods are feasible. In this paper, we present an automated stent strut detection technique which requires neither lumen nor vessel wall segmentation. To detect strut-pixel candidates, both global intensity histograms and local intensity profiles of the raw polar images are used. Gaussian smoothing is applied followed by specified Prewitt compass filters to detect the trailing shadow of each strut. The shadow edge positions assist the strut-pixel candidates clustering. In the end, a 3D guide wire filter is applied to remove the guide wire from the detection results. For validation, two experts marked 6738 struts in 1021 frames in 10 IVOCT image sequences from a one-year follow up study. The struts were labeled as malapposed, apposed or covered together with the image quality (high, medium, low). The inter-observer agreement was 96%. The algorithm was validated for different combinations of strut status and image quality. Compared to the manual results, 93% of the struts were correctly detected by the new method. For each combination, the lowest accuracy was 88%, which shows the robustness towards different situations. The presented method can detect struts automatically regardless of the strut status or the image quality, which can be used for quantitative measurement, 3D reconstruction and visualization of the implanted stents.