ArticleInternational journal of computer assisted radiology and surgery2021
Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- World's First Real-Time Artificial Intelligence-Assisted Mechanical Thrombectomy for Acute Ischemic Stroke.AJNR. American journal of neuroradiology · 2025Article
- World's First Artificial Intelligence-Based Evaluation of Rist Catheter Stability in Transradial Procedures: A Feasibility Study.Journal of neuroendovascular therapy · 2025Article
- Computer Aided Intracranial Aneurysm Treatment Based on 2D/3D Mapping, Virtual Deployment and Online Distal Marker Detection.Cardiovascular engineering and technology · 2024Article
- Impact of cardiac and respiratory motion on the 3D accuracy of image-guided interventions on monoplane systems.International journal of computer assisted radiology and surgery · 2024Article
- 3D localization from 2D X-ray projection.International journal of computer assisted radiology and surgery · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeAutomatic identification of interventional devices in X-ray (XR) fluoroscopy offers the potential of improved navigation during transcatheter endovascular procedures. This paper presents a prototype implementation of fully automatic 3D reconstruction of a cryo-balloon catheter during pulmonary vein isolation (PVI) procedures by deep learning approaches.
methodsWe employ convolutional neural networks (CNN) to automatically identify the cryo-balloon XR marker and catheter shaft in 2D fluoroscopy during PVI. Training data are generated exploiting established semiautomatic techniques, including template-matching and analytical graph building. A first network of U-net architecture uses a single grayscale XR image as input and yields the mask of the XR marker. A second network of the similar architecture is trained using the mask of the XR marker as additional input to the grayscale XR image for the segmentation of the cryo-balloon catheter shaft mask. The structures automatically identified in two 2D images with different angulations are then used to reconstruct the cryo-balloon in 3D.
resultsAutomatic identification of the XR marker was successful in 78% of test cases and in 100% for the catheter shaft. Training of the model for prediction of the XR marker mask was successful with 3426 training samples. Incorporation of the XR marker mask as additional input for the model predicting the catheter shaft allowed to achieve good training result with only 805 training samples. The average prediction time per frame was 14.47 ms for the XR marker and 78.22 ms for the catheter shaft. Localization accuracy for the XR marker yielded on average 1.52 pixels or 0.56 mm.
conclusionsIn this paper, we report a novel method for automatic detection and 3D reconstruction of the cryo-balloon catheter shaft and marker from 2D fluoroscopic images. Initial evaluation yields promising results thus indicating the high potential of CNNs as alternatives to the current state-of-the-art solutions.
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