A Hybrid Catheter Localisation Framework in Echocardiography Based on Electromagnetic Tracking and Deep Learning Segmentation

Abstract

Interventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases and aortic diseases. Ultrasound imaging, also called echocardiography, is a typical imaging tool that monitors catheter puncturing. Localising a medical device accurately during cardiac interventions can help improve the procedure’s safety and reliability under ultrasound imaging. However, external device tracking and image-based tracking methods can only provide a partial solution. Thus, we proposed a hybrid framework, with the combination of both methods to localise the catheter tip target in an automatic way. The external device used was an electromagnetic tracking system from North Digital Inc (NDI) and the ultrasound image analysis was based on UNet, a deep learning network for semantic segmentation. From the external method, the tip’s location was determined precisely, and the deep learning platform segmented the exact catheter tip automatically. This novel hybrid localisation framework combines the advantages of external electromagnetic (EM) tracking and deep-learning-based image method, which offers a new solution to identify the moving medical device in low-resolution ultrasound images.Featured ApplicationThis framework can be applied to other medical-device localisation fields to help doctors identify a moving target in low-resolution ultrasound images.

Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence

“…Figure 1 shows the basic framework of the tracing algorithm presented in this article. First, the video boundary is segmented using MaskR-CNN, and afterward the up-and-comer areas acquired from the divided pictures are utilized as the contribution of our superior calculation [ 18 ]. In this way, the foreground tracking target can be expressed more clearly and tracking range can be reduced.…”