Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures

Yingliang Ma; Vishal S Mehta; C. Aldo Rinaldi; Pengpeng Hu; Steven Niederer; Reza Razavi

doi:10.1007/978-3-031-35302-4_32

Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures

Yingliang Ma, Vishal S Mehta, C. Aldo Rinaldi, Pengpeng Hu, Steven Niederer, Reza Razavi

Research Centre for Computational Science and Mathematical Modelling

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

2 Citations (Scopus)

12 Downloads (Pure)

Abstract

The lead extraction procedures are for the patients who already have pacemaker implanted and leads need to be replaced. The procedure is a high-risk procedure and it could lead to major complications or even procedure-related death. Recently, an Electra Registry Outcome Score (EROS) was designed to create a risk assessment tool using the data about personal health records and an accuracy of 0.70 was achieved. In this paper, we hypothesized that a coil inside the superior vena cava (SVC) is a very important risk factor. By integrating it into the risk assessment model, the accuracy can be further improved. Therefore, an automatic detection method was developed to localize the positions of coils in the X-ray images. It was based on a U-Net convolutional network. To determine the coil position relative to the SVC position inside the chest X-ray image, the heart region was first detected by using a modified VGG16 model. Then, the bounding box of the SVC can be estimated based on the heart anatomy. Finally, a XGBoost classifier was trained on the data about personal health records and the risk factor about the coil position. An accuracy of 0.85 was achieved.

Original language	English
Title of host publication	Functional Imaging and Modeling of the Heart
Subtitle of host publication	12th International Conference, FIMH 2023, Proceedings
Editors	Olivier Bernard, Patrick Clarysse, Nicolas Duchateau, Jacques Ohayon, Magalie Viallon
Publisher	Springer
Pages	310-319
Number of pages	10
ISBN (Electronic)	978-3-031-35302-4
ISBN (Print)	978-3-031-35301-7
DOIs	https://doi.org/10.1007/978-3-031-35302-4_32
Publication status	E-pub ahead of print - 16 Jun 2023
Event	12th International Conference On Functional Imaging And Modeling Of The Heart - Lyon, France Duration: 19 Jun 2023 → 22 Jun 2023 https://fimh2023.sciencesconf.org/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13958
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference On Functional Imaging And Modeling Of The Heart
Abbreviated title	FIMH2023
Country/Territory	France
City	Lyon
Period	19/06/23 → 22/06/23
Internet address	https://fimh2023.sciencesconf.org/

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-031-35302-4_32

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

deep learning
wire detection
risk assessment

Access to Document

10.1007/978-3-031-35302-4_32

Hu2023AAMAccepted author manuscript, 2.63 MB

Cite this

Ma, Y., Mehta, V. S., Rinaldi, C. A., Hu, P., Niederer, S., & Razavi, R. (2023). Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures. In O. Bernard, P. Clarysse, N. Duchateau, J. Ohayon, & M. Viallon (Eds.), Functional Imaging and Modeling of the Heart : 12th International Conference, FIMH 2023, Proceedings (pp. 310-319). (Lecture Notes in Computer Science; Vol. 13958). Springer. Advance online publication. https://doi.org/10.1007/978-3-031-35302-4_32

Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures. / Ma, Yingliang; Mehta, Vishal S; Rinaldi, C. Aldo et al.
Functional Imaging and Modeling of the Heart : 12th International Conference, FIMH 2023, Proceedings. ed. / Olivier Bernard; Patrick Clarysse; Nicolas Duchateau; Jacques Ohayon; Magalie Viallon. Springer, 2023. p. 310-319 (Lecture Notes in Computer Science; Vol. 13958).

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

Ma, Y, Mehta, VS, Rinaldi, CA, Hu, P, Niederer, S & Razavi, R 2023, Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures. in O Bernard, P Clarysse, N Duchateau, J Ohayon & M Viallon (eds), Functional Imaging and Modeling of the Heart : 12th International Conference, FIMH 2023, Proceedings. Lecture Notes in Computer Science, vol. 13958, Springer, pp. 310-319, 12th International Conference On Functional Imaging And Modeling Of The Heart, Lyon, France, 19/06/23. https://doi.org/10.1007/978-3-031-35302-4_32

Ma Y, Mehta VS, Rinaldi CA, Hu P, Niederer S, Razavi R. Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures. In Bernard O, Clarysse P, Duchateau N, Ohayon J, Viallon M, editors, Functional Imaging and Modeling of the Heart : 12th International Conference, FIMH 2023, Proceedings. Springer. 2023. p. 310-319. (Lecture Notes in Computer Science). Epub 2023 Jun 16. doi: 10.1007/978-3-031-35302-4_32

Ma, Yingliang ; Mehta, Vishal S ; Rinaldi, C. Aldo et al. / Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures. Functional Imaging and Modeling of the Heart : 12th International Conference, FIMH 2023, Proceedings. editor / Olivier Bernard ; Patrick Clarysse ; Nicolas Duchateau ; Jacques Ohayon ; Magalie Viallon. Springer, 2023. pp. 310-319 (Lecture Notes in Computer Science).

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AU - Niederer, Steven

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N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-031-35302-4_32 Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

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N2 - The lead extraction procedures are for the patients who already have pacemaker implanted and leads need to be replaced. The procedure is a high-risk procedure and it could lead to major complications or even procedure-related death. Recently, an Electra Registry Outcome Score (EROS) was designed to create a risk assessment tool using the data about personal health records and an accuracy of 0.70 was achieved. In this paper, we hypothesized that a coil inside the superior vena cava (SVC) is a very important risk factor. By integrating it into the risk assessment model, the accuracy can be further improved. Therefore, an automatic detection method was developed to localize the positions of coils in the X-ray images. It was based on a U-Net convolutional network. To determine the coil position relative to the SVC position inside the chest X-ray image, the heart region was first detected by using a modified VGG16 model. Then, the bounding box of the SVC can be estimated based on the heart anatomy. Finally, a XGBoost classifier was trained on the data about personal health records and the risk factor about the coil position. An accuracy of 0.85 was achieved.

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Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures

Abstract

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Keywords

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