Cascade Attention Machine for Occluded Landmark Detection in 2D X-Ray Angiography

In cardiac interventions, localization of guiding catheter tip in 2D fluoroscopic images is important to specify ves-sel branches and calibrate vessels with stenosis. While detection of guiding catheter tip is not trivial in contrast-free images due to low dose radiation as well as occlusion by other devices, it is even more challenging in contrast-filled images. As contrast-filled vessels become visible in X-ray imaging, the landmark of guiding catheter tip can often be completely occluded by the contrast medium. It is difficult even for human eyes to precisely localize the catheter tip from a single angiography image. Physicians have to rely on information before the inject of contrast medium to localize the guiding catheter tip occluded by contrast medium. Automatic landmark detection when occlusion happens is important and can significantly simplify the intervention workflow. To address this problem, we propose a novel Cascade Attention Machine (CAM) model. It borrows the idea of how human experts localize the catheter tip by first per-forming landmark detection when occlusion does not hap-pen, then leveraging this information as prior knowledge to assist the occluded detection. Attention maps are computed from non-occluded detection to further refine the heatmaps for occluded detection to guide the inference focusing on related regions. Experiments on X-ray angiography demonstrate the promising performance compared with the state-of-the-art baselines. It shows that the CAM can capture the relation between situations with and without occlusion to achieve precise detection of occluded landmark.