A feasibility study for Deep Learning Image Guided Guidewire Tracking for Image-guided Interventions
A current challenge in real-time magnetic resonance imaging (MRI) guided minimally invasive images is needle tracking and planning. We propose a pipeline for automatic object detection using a state-of-the-art object detection network. Predictions by the object detection network were used to translate the MRI plane to keep a guidewire tip in a plane. We evaluated the pipeline on displacement error between the prediction and the actual location of the guidewire tip in a setup with an anthropomorphic blood vessel. For this setup, we hypothesized that the network should be able to correctly predict the actual location within a margin of 10 mm, at least within 1000 ms.
Results show that the pipeline can accurately track the guidewire tip in real-time (within 458 ms), with a mean displacement error of 7 mm (s = 4). Based on this evidence, we have demonstrated the feasibility of deep learning assisted image-guided interventions, creating possibilities for other deep learning guided interventions. Our proposed method shows potential for cryoablation. During these types of minimally invasive procedures tracking needles can be a challenge.