English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Augmenting X-ray imaging with 3D roadmap to improve guidance is a commonstrategy. Such approaches benefit from automated analysis of the X-ray images,such as the automatic detection and tracking of instruments. In this paper, wepropose a real-time method to segment the catheter and guidewire in 2D X-rayfluoroscopic sequences. The method is based on deep convolutional neuralnetworks. The network takes as input the current image and the three previousones, and segments the catheter and guidewire in the current image.Subsequently, a centerline model of the catheter is constructed from thesegmented image. A small set of annotated data combined with data augmentationis used to train the network. We trained the method on images from 182 X-raysequences from 23 different interventions. On a testing set with images of 55X-ray sequences from 5 other interventions, a median centerline distance errorof 0.2 mm and a median tip distance error of 0.9 mm was obtained. Thesegmentation of the instruments in 2D X-ray sequences is performed in areal-time fully-automatic manner.

Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy