{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fully-automatic-and-real-time-catheter","title":"Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy","arxiv_id":"1707.05137","date":"2017-07-17","proceeding":null,"authors":["Pierre Ambrosini","Daniel Ruijters","Wiro J. Niessen","Adriaan Moelker","Theo van Walsum"],"abstract":"Augmenting X-ray imaging with 3D roadmap to improve guidance is a common\nstrategy. Such approaches benefit from automated analysis of the X-ray images,\nsuch as the automatic detection and tracking of instruments. In this paper, we\npropose a real-time method to segment the catheter and guidewire in 2D X-ray\nfluoroscopic sequences. The method is based on deep convolutional neural\nnetworks. The network takes as input the current image and the three previous\nones, and segments the catheter and guidewire in the current image.\nSubsequently, a centerline model of the catheter is constructed from the\nsegmented image. A small set of annotated data combined with data augmentation\nis used to train the network. We trained the method on images from 182 X-ray\nsequences from 23 different interventions. On a testing set with images of 55\nX-ray sequences from 5 other interventions, a median centerline distance error\nof 0.2 mm and a median tip distance error of 0.9 mm was obtained. The\nsegmentation of the instruments in 2D X-ray sequences is performed in a\nreal-time fully-automatic manner.","url_abs":"http://arxiv.org/abs/1707.05137v1","url_pdf":"http://arxiv.org/pdf/1707.05137v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fully-automatic-and-real-time-catheter","repo_url":"https://github.com/pambros/CNN-2D-X-Ray-Catheter-Detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}