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

YingLiang Ma, Mazen Alhrishy, Maria Panayiotou, Srinivas Ananth Narayan, Ansab Fazili, Peter Mountney, Kawal S. Rhode

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

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 London Ltd
Pages172-182
Number of pages11
Volume10263 LNCS
ISBN (Print)9783319594477
DOIs
Publication statusPublished - 23 May 2017
Event9th International Conference on Functional Imaging and Modelling of the Heart - Toronto, Canada
Duration: 11 Jun 201713 Jun 2017
Conference number: 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Functional Imaging and Modelling of the Heart
Abbreviated titleFIMH 2017
CountryCanada
CityToronto
Period11/06/1713/06/17

Fingerprint

Catheters
Real-time
X rays
Enhancement
Wire
Cardiac
Vessel
Learning algorithms
Dosimetry
Learning systems
Learning Algorithm
Dose
Machine Learning
Congenital Heart Disease
Filter
3D Imaging
Pediatrics
Energy Method
Image Sequence
Visibility

Keywords

  • Target Object
  • Image Artifact
  • Image Mask
  • Line Block
  • Vessel Filter

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ma, Y., Alhrishy, M., Panayiotou, M., Narayan, S. A., Fazili, A., Mountney, P., & Rhode, K. S. (2017). Real-time guiding catheter and guidewire detection for congenital cardiovascular interventions. In Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings (Vol. 10263 LNCS, pp. 172-182). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10263 LNCS). Springer-Verlag London Ltd. https://doi.org/10.1007/978-3-319-59448-4_17

Real-time guiding catheter and guidewire detection for congenital cardiovascular interventions. / Ma, YingLiang; Alhrishy, Mazen; Panayiotou, Maria; Narayan, Srinivas Ananth; Fazili, Ansab; Mountney, Peter; Rhode, Kawal S.

Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Vol. 10263 LNCS Springer-Verlag London Ltd, 2017. p. 172-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10263 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Ma, Y, Alhrishy, M, Panayiotou, M, Narayan, SA, Fazili, A, Mountney, P & Rhode, KS 2017, Real-time guiding catheter and guidewire detection for congenital cardiovascular interventions. in Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. vol. 10263 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10263 LNCS, Springer-Verlag London Ltd, pp. 172-182, 9th International Conference on Functional Imaging and Modelling of the Heart , Toronto, Canada, 11/06/17. https://doi.org/10.1007/978-3-319-59448-4_17
Ma Y, Alhrishy M, Panayiotou M, Narayan SA, Fazili A, Mountney P et al. Real-time guiding catheter and guidewire detection for congenital cardiovascular interventions. In Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Vol. 10263 LNCS. Springer-Verlag London Ltd. 2017. p. 172-182. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-59448-4_17
Ma, YingLiang ; Alhrishy, Mazen ; Panayiotou, Maria ; Narayan, Srinivas Ananth ; Fazili, Ansab ; Mountney, Peter ; Rhode, Kawal S. / Real-time guiding catheter and guidewire detection for congenital cardiovascular interventions. Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Vol. 10263 LNCS Springer-Verlag London Ltd, 2017. pp. 172-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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