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Related Experiment Video

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Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net.

Ina Vernikouskaya1, Dagmar Bertsche2, Tillman Dahme2

  • 1Department of Internal Medicine II, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081, Ulm, Germany. ina.vernikouskaya@uni-ulm.de.

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PubMed
Summary

This study presents a novel deep learning method for automatically identifying and reconstructing cryo-balloon catheters in X-ray fluoroscopy during pulmonary vein isolation procedures. The approach shows promising results for improved navigation in endovascular interventions.

Keywords:
Automatic segmentationCryo-balloonReconstructionSemi-automatic annotationUnet

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Area of Science:

  • Medical Imaging
  • Interventional Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Transcatheter endovascular procedures require precise navigation.
  • Accurate visualization of interventional devices like cryo-balloon catheters is crucial for procedural success.
  • Current methods for device visualization may have limitations.

Purpose of the Study:

  • To develop a fully automatic 3D reconstruction method for cryo-balloon catheters using deep learning.
  • To enhance navigation during pulmonary vein isolation (PVI) procedures.
  • To explore the potential of convolutional neural networks (CNNs) for interventional device identification.

Main Methods:

  • Utilized U-net architecture CNNs for automatic identification of cryo-balloon markers and catheter shafts in 2D fluoroscopy.
  • Generated training data using semi-automatic techniques like template-matching.
  • Reconstructed the cryo-balloon in 3D by combining information from two 2D images with different angulations.

Main Results:

  • Achieved 78% success in automatic identification of the X-ray (XR) marker and 100% for the catheter shaft.
  • Demonstrated efficient training with fewer samples when using marker masks as additional input.
  • Reported an average prediction time of 14.47 ms for the XR marker and 78.22 ms for the catheter shaft.
  • Achieved a localization accuracy of 0.56 mm for the XR marker.

Conclusions:

  • Successfully developed a novel method for automatic detection and 3D reconstruction of cryo-balloon catheters from 2D fluoroscopic images.
  • The deep learning approach shows high potential as an alternative to current state-of-the-art visualization solutions.
  • Promising initial results indicate the feasibility and effectiveness of CNNs in this application.