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Torabinia et al. Mini-invasive Surg 2021;5:32 Mini-invasive Surgery
DOI: 10.20517/2574-1225.2021.63

Technical Note Open Access

Deep learning-driven catheter tracking from bi-plane

X-ray fluoroscopy of 3D printed heart phantoms

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Matin Torabinia , Alexandre Caprio , Sun-Joo Jang , Tianyu Ma , Honson Tran , Lina Mekki , Isabella
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Chen , Mert Sabuncu , S. Chiu Wong , Bobak Mosadegh 1,2
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Dalio Institute of Cardiovascular Imaging, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY 10021,
USA.
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Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA.
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School of Electrical and Computer Engineering, Cornell Univesity, Ithaca, NY 10021, USA.
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Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
Correspondence to: Dr. Bobak Mosadegh, Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-
Presbyterian Hospital and Weill Cornell Medicine, 1196 York Avenue, Bronk 908B, New York, NY 10065, USA.
E-mail: bom2008@med.cornell.edu
How to cite this article: Torabinia M, Caprio A, Jang SJ, Ma T, Tran H, Mekki L, Chen I, Sabuncu M, Wong SC, Mosadegh B. Deep
learning-driven catheter tracking from bi-plane X-ray fluoroscopy of 3D printed heart phantoms. Mini-invasive Surg 2021;5:32.
https://dx.doi.org/10.20517/2574-1225.2021.63
Received: 8 May 2021 First Decision: 25 May 2021 Revised: 27 May 2021 Accepted: 7 Jun 2021 First online: 9 Jun 2021
Academic Editors: Bobak Mosadegh, Giulio Belli Copy Editor: Xi-Jun Chen Production Editor: Xi-Jun Chen
Abstract
Minimally invasive surgery (MIS) has changed not only the performance of specific operations but also the more
effective strategic approach to all surgeries. Expansion of MIS to more complex surgeries demands further
development of new technologies, including robotic surgical systems, navigation, guidance, visualizations, dexterity
enhancement, and 3D printing technology. In the cardiovascular domain, 3D printed modeling can play a crucial
role in providing improved visualization of the anatomical details and guide precision operations as well as
functional evaluation of various congenital and congestive heart conditions. In this work, we propose a novel deep
learning-driven tracking method for providing quantitative 3D tracking of mock cardiac interventions on custom-
designed 3D printed heart phantoms. In this study, the position of the tip of a catheter is tracked from bi-plane
fluoroscopic images. The continuous positioning of the catheter relative to the 3D printed model was co-registered
in a single coordinate system using external fiducial markers embedded into the model. Our proposed method has
the potential to provide quantitative analysis for training exercises of percutaneous procedures guided by bi-plane
fluoroscopy.

© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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