Purpose: Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation time 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, 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. Method: We propose a fast, learning-based method to segment aortic stents in 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. Results: We use 36 fluoroscopies acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coecient of 0.932 when using X-ray alone, and 0.944 and 0.915 when adding the preoperative model and information about the expected wire, respectively. Conclusion: The proposed method is fully-automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. By comparing the segmentation with the preoperative model, the quality of the intraoperative fusion can be assessed.
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Purpose: Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation time 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, an implanted stent can be used to adjust the fusion. However, de...
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