Self-Supervised Learning for Interventional Image Analytics: Towards Robust Device Trackers

arXiv:2405.01156v1 Announce Type: new
Abstract: An accurate detection and tracking of devices such as guiding catheters in live X-ray image acquisitions is an essential prerequisite for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness no failures during tracking. To achieve that, one needs to efficiently tackle challenges, such as: device obscuration by contrast agent or other external devices or wires, changes …

abstract acquisitions analytics arxiv cs.ai cs.cv detection devices guidance image image analytics information ray robust safety self-supervised learning supervised learning tracking type x-ray