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A Merging Model Reconstruction Method for Image-Guided Gastroscopic Biopsy

机译:一种用于图像引导胃镜活组织检查的合并模型重构方法

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Tattooing and Argon plasma coagulation (APC) are used for gastroscopic biopsy traditionally.To overcome the invasive issues of tattooing and APC, we proposed an image-guided gastroscopic biopsy system (IGGBS) to guide endoscopist in retargeting previous biopsy sites in the follow-ups non-invasively.In this paper, a model merging method is proposed to improve the IGGBS's accuracy.The method reconstructs local realistic model based on gastroscopic image sequences during procedure, then the local model is merged with the pre-operative model in real-time.As a consequence, the merging model is used in IGGBS to navigate endoscopist to retarget biopsy sites, which provides the endoscopist with a confident 3D region around the endoscope camera site and a measure of the reconstruction precision.As the experimental result shows, the root mean square target registration error of IGGBS using merging model is 9.5 mm, which is close to the conventional biopsy tattooing method (about lcm).
机译:传统上用于胃镜活组织检查的纹身和氩气相凝血非侵入。在本文中,提出了一种模型合并方法来提高IgGBS的准确性。该方法在过程中重建基于胃镜图像序列的局部现实模型,然后将本地模型实时与术前模型合并.as,后果,合并模型用于IGGBS,用于导航内窥镜师以围绕内窥镜摄像机站点周围的带有自信3D区域的内窥镜师和重建精度。实验结果显示,根本使用合并模型的IGGBS的平均方形目标登记误差为9.5毫米,接近常规的活检纹身方法(关于LCM)。

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