  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.
