Cathnets: Detection and single-view depth prediction of catheter electrodes

Christoph Baur; Shadi Albarqouni; Stefanie Demirci; Nassir Navab; Pascal Fallavollita

doi:10.1007/978-3-319-43775-0_4

Cathnets: Detection and single-view depth prediction of catheter electrodes

Christoph Baur
, Shadi Albarqouni
, Stefanie Demirci
, Nassir Navab
, Pascal Fallavollita

Informatics 16 - Chair of Computer Aided Medical Procedures

Technical University of Munich
Johns Hopkins University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

The recent success of convolutional neural networks in many computer vision tasks implies that their application could also be beneficial for vision tasks in cardiac electrophysiology procedures which are commonly carried out under guidance of C-arm fluoroscopy. Many efforts for catheter detection and reconstruction have been made, but especially robust detection of catheters in X-ray images in realtime is still not entirely solved. We propose two novel methods for (i) fully automatic electrophysiology catheter electrode detection in interventional X-ray images and (ii) single-view depth estimation of such electrodes based on convolutional neural networks. For (i), experiments on 24 different fluoroscopy sequences (1650 X-ray images) yielded a detection rate >99 %. Our experiments on (ii) depth prediction using 20 images with depth information available revealed that we are able to estimate the depth of catheter tips in the lateral view with a remarkable mean error of 6.08 ± 4.66 mm.

Original language	English
Title of host publication	Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings
Editors	Hongen Liao, Guoyan Zheng, Su-Lin Lee, Philippe Cattin, Pierre Jannin
Publisher	Springer Verlag
Pages	38-49
Number of pages	12
ISBN (Print)	9783319437743
DOIs	https://doi.org/10.1007/978-3-319-43775-0_4
State	Published - 2016
Event	7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 - Bern, Switzerland Duration: 24 Aug 2016 → 26 Aug 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9805 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016
Country/Territory	Switzerland
City	Bern
Period	24/08/16 → 26/08/16

Keywords

Catheter detection
Convolutional neural network
Depth prediction
Electrophysiology
Interventional imaging

Access to Document

10.1007/978-3-319-43775-0_4

Cite this

Baur, C., Albarqouni, S., Demirci, S., Navab, N., & Fallavollita, P. (2016). Cathnets: Detection and single-view depth prediction of catheter electrodes. In H. Liao, G. Zheng, S.-L. Lee, P. Cattin, & P. Jannin (Eds.), Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings (pp. 38-49). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9805 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-43775-0_4

Baur, Christoph ; Albarqouni, Shadi ; Demirci, Stefanie et al. / Cathnets : Detection and single-view depth prediction of catheter electrodes. Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. editor / Hongen Liao ; Guoyan Zheng ; Su-Lin Lee ; Philippe Cattin ; Pierre Jannin. Springer Verlag, 2016. pp. 38-49 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0a0bba2fc9844ebca855175c46baf955,

title = "Cathnets: Detection and single-view depth prediction of catheter electrodes",

abstract = "The recent success of convolutional neural networks in many computer vision tasks implies that their application could also be beneficial for vision tasks in cardiac electrophysiology procedures which are commonly carried out under guidance of C-arm fluoroscopy. Many efforts for catheter detection and reconstruction have been made, but especially robust detection of catheters in X-ray images in realtime is still not entirely solved. We propose two novel methods for (i) fully automatic electrophysiology catheter electrode detection in interventional X-ray images and (ii) single-view depth estimation of such electrodes based on convolutional neural networks. For (i), experiments on 24 different fluoroscopy sequences (1650 X-ray images) yielded a detection rate >99 \%. Our experiments on (ii) depth prediction using 20 images with depth information available revealed that we are able to estimate the depth of catheter tips in the lateral view with a remarkable mean error of 6.08 ± 4.66 mm.",

keywords = "Catheter detection, Convolutional neural network, Depth prediction, Electrophysiology, Interventional imaging",

author = "Christoph Baur and Shadi Albarqouni and Stefanie Demirci and Nassir Navab and Pascal Fallavollita",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 ; Conference date: 24-08-2016 Through 26-08-2016",

year = "2016",

doi = "10.1007/978-3-319-43775-0\_4",

language = "English",

isbn = "9783319437743",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings",

}

Baur, C, Albarqouni, S, Demirci, S, Navab, N & Fallavollita, P 2016, Cathnets: Detection and single-view depth prediction of catheter electrodes. in H Liao, G Zheng, S-L Lee, P Cattin & P Jannin (eds), Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9805 LNCS, Springer Verlag, pp. 38-49, 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016, Bern, Switzerland, 24/08/16. https://doi.org/10.1007/978-3-319-43775-0_4

Cathnets: Detection and single-view depth prediction of catheter electrodes. / Baur, Christoph; Albarqouni, Shadi; Demirci, Stefanie et al.
Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. ed. / Hongen Liao; Guoyan Zheng; Su-Lin Lee; Philippe Cattin; Pierre Jannin. Springer Verlag, 2016. p. 38-49 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9805 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cathnets

T2 - 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016

AU - Baur, Christoph

AU - Albarqouni, Shadi

AU - Demirci, Stefanie

AU - Navab, Nassir

AU - Fallavollita, Pascal

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - The recent success of convolutional neural networks in many computer vision tasks implies that their application could also be beneficial for vision tasks in cardiac electrophysiology procedures which are commonly carried out under guidance of C-arm fluoroscopy. Many efforts for catheter detection and reconstruction have been made, but especially robust detection of catheters in X-ray images in realtime is still not entirely solved. We propose two novel methods for (i) fully automatic electrophysiology catheter electrode detection in interventional X-ray images and (ii) single-view depth estimation of such electrodes based on convolutional neural networks. For (i), experiments on 24 different fluoroscopy sequences (1650 X-ray images) yielded a detection rate >99 %. Our experiments on (ii) depth prediction using 20 images with depth information available revealed that we are able to estimate the depth of catheter tips in the lateral view with a remarkable mean error of 6.08 ± 4.66 mm.

AB - The recent success of convolutional neural networks in many computer vision tasks implies that their application could also be beneficial for vision tasks in cardiac electrophysiology procedures which are commonly carried out under guidance of C-arm fluoroscopy. Many efforts for catheter detection and reconstruction have been made, but especially robust detection of catheters in X-ray images in realtime is still not entirely solved. We propose two novel methods for (i) fully automatic electrophysiology catheter electrode detection in interventional X-ray images and (ii) single-view depth estimation of such electrodes based on convolutional neural networks. For (i), experiments on 24 different fluoroscopy sequences (1650 X-ray images) yielded a detection rate >99 %. Our experiments on (ii) depth prediction using 20 images with depth information available revealed that we are able to estimate the depth of catheter tips in the lateral view with a remarkable mean error of 6.08 ± 4.66 mm.

KW - Catheter detection

KW - Convolutional neural network

KW - Depth prediction

KW - Electrophysiology

KW - Interventional imaging

UR - https://www.scopus.com/pages/publications/84984829668

U2 - 10.1007/978-3-319-43775-0_4

DO - 10.1007/978-3-319-43775-0_4

M3 - Conference contribution

AN - SCOPUS:84984829668

SN - 9783319437743

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 38

EP - 49

BT - Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings

A2 - Liao, Hongen

A2 - Zheng, Guoyan

A2 - Lee, Su-Lin

A2 - Cattin, Philippe

A2 - Jannin, Pierre

PB - Springer Verlag

Y2 - 24 August 2016 through 26 August 2016

ER -

Baur C, Albarqouni S, Demirci S, Navab N, Fallavollita P. Cathnets: Detection and single-view depth prediction of catheter electrodes. In Liao H, Zheng G, Lee SL, Cattin P, Jannin P, editors, Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Springer Verlag. 2016. p. 38-49. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-43775-0_4

Cathnets: Detection and single-view depth prediction of catheter electrodes

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