论文标题
从内窥镜视频中重建鼻窦解剖学 - 一种无辐射方法进行定量纵向评估
Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
论文作者
论文摘要
直接从内窥镜视频中重建精确的鼻窦解剖结构的3D表面模型是进行横截面和纵向分析的有前途的途径，以更好地了解鼻窦解剖结构与外科手术结局之间的关系。我们提出了一种基于患者的，基于学习的方法，可直接直接从内窥镜视频中进行3D重建。我们证明了我们方法在IN和EX VIVO数据中的有效性和准确性，在这些数据中，我们将其与运动，COLMAP的密集重建以及CT的地面真相解剖结构相比进行了比较。我们的纹理重建是水密性的，可以与CT吻合，可以测量临床相关参数。源代码可在https://github.com/lppllppl920/densereconstruction-pytorch上找到。
Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understand the relationship between sinus anatomy and surgical outcomes. We present a patient-specific, learning-based method for 3D reconstruction of sinus surface anatomy directly and only from endoscopic videos. We demonstrate the effectiveness and accuracy of our method on in and ex vivo data where we compare to sparse reconstructions from Structure from Motion, dense reconstruction from COLMAP, and ground truth anatomy from CT. Our textured reconstructions are watertight and enable measurement of clinically relevant parameters in good agreement with CT. The source code is available at https://github.com/lppllppl920/DenseReconstruction-Pytorch.
