ArticleInternational journal of computer assisted radiology and surgery2018
Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 36 citations in OpenAlex.
- Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- Clinical Implementation and Performance of Real-Time AI System for Device Tracking in Neurointervention: First U.S. Evaluation of Neuro-Vascular Assist.Clinical neuroradiology · 2026Article
- Deep learning-based biliary stent classification and transfer learning adaptation to an additional stent type.European radiology experimental · 2026Article
- Deep Learning-Based Intraoperative Stent Graft Segmentation on Completion Digital Subtraction Angiography During Endovascular Aneurysm Repair.Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists · 2023Article
- Prediction of guidewire-induced aortic deformations during EVAR: a finite element andFrontiers in physiology · 2023Article
- Article
- Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration.International journal of computer assisted radiology and surgery · 2020Article
- Simultaneous reconstruction of multiple stiff wires from a single X-ray projection for endovascular aortic repair.International journal of computer assisted radiology and surgery · 2019Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible.
methodWe propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation.
resultsWe use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively.
conclusionThe proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.
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