Objectives: fenestration of stent-graft (IVFS) demands high-precision navigation methods to achieve optimal surgical outcomes. This study aims to propose an augmented reality (AR) navigation method for IVFS, which can provide overlay display to locate fenestration positions.
Methods: We propose an AR navigation method to assist doctors in performing IVFS. A deep learning-based aorta segmentation algorithm is used to achieve automatic and rapid aorta segmentation. The Vuforia-based virtual-real registration and marker recognition algorithm are integrated to ensure accurate AR image.
Results: The proposed method can provide three-dimensional AR image, and the fiducial registration error after virtual-real registration is 2.070 mm. The aorta segmentation experiment obtains dice similarity coefficient of 91.12% and Hausdorff distance of 2.59, better than conventional algorithms before improvement.
Conclusions: The proposed method can intuitively and accurately locate fenestration positions, and therefore can assist doctors in performing IVFS.
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| http://dx.doi.org/10.1080/24699322.2023.2289339 | DOI Listing |
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