Real-time guiding catheter and guidewire detection for congenital cardiovascular interventions

Ying Liang Ma*, Mazen Alhrishy, Maria Panayiotou, Siri Ananth Narayan, Ansab Fazili, Peter Mountney, Kawal Rhode

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingOther chapter contributionpeer-review


Guiding catheters and guidewires are used extensively in pediatric cardiac catheterization procedures for congenital heart diseases (CHD). Detecting their positions in fluoroscopic X-ray images is important for several clinical applications, such as visibility enhancement for low dose X-ray images, and co-registration between 2D and 3D imaging modalities. As guiding catheters are made from thin plastic tubes, they can be deformed by cardiac and breathing motions. Therefore, detection is the essential step before automatic tracking of guiding catheters in live X-ray fluoroscopic images. However, there are several wire-like artifacts existing in X-ray images, which makes developing a real-time robust detection method very challenging. To solve those challenges in real-time, a localized machine learning algorithm is built to distinguish between guiding catheters and artifacts. As the machine learning algorithm is only applied to potential wire-like objects, which are obtained from vessel enhancement filters, the detection method is fast enough to be used in real-time applications. The other challenge is the low contrast between guiding catheters and background, as the majority of X-ray images are low dose. Therefore, the guiding catheter might be detected as a discontinuous curve object, such as a few disconnected line blocks from the vessel enhancement filter. A minimum energy method is developed to trace the whole wire object. Finally, the proposed methods are tested on 1102 images which are from 8 image sequences acquired from 3 clinical cases. Results show an accuracy of 0.87 ± 0.53 mm which is measured as the error distances between the detected object and the manually annotated object. The success rate of detection is 83.4%.

Original languageEnglish
Title of host publicationFunctional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
PublisherSpringer Verlag
Number of pages11
Volume10263 LNCS
ISBN (Print)9783319594477
Publication statusPublished - 23 May 2017
Event9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 - Toronto, Canada
Duration: 11 Jun 201713 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10263 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017


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