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Implantation of stents into coronary arteries is a common treatment option for patients with cardiovascular disease. Assessment of safety and efficacy of the stent implantation occurs via manual visual inspection of the neointimal coverage from intravascular optical coherence tomography (IVOCT) images. However, such manual assessment requires the detection of thousands of strut points within the stent. This is a challenging, tedious, and time-consuming task because the strut points usually appear as small, irregular shaped objects with inhomogeneous textures, and are often occluded by shadows, artifacts, and vessel walls. Conventional methods based on textures, edge detection, or simple classifiers for automated detection of strut points in IVOCT images have low recall and precision as they are, unable to adequately represent the visual features of the strut point for detection. In this study, we propose a local-global refinement network to integrate local-patch content with global content for strut points detection from IVOCT images. Our method densely detects the potential strut points in local image patches and then refines them according to global appearance constraints to reduce false positives. Our experimental results on a clinical dataset of 7,000 IVOCT images demonstrated that our method outperformed the state-of-the-art methods with a recall of 0.92 and precision of 0.91 for strut points detection.

Deep Local-Global Refinement Network for Stent Analysis in IVOCT Images