基于单目内窥镜视频的鼻窦解剖结构密集三维重建定量评估
A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video
AI总结：
本文定量评估了基于单目内窥镜视频的自监督鼻窦三维重建方法，发现其解剖结构重建误差为0.91 mm，但导航配准误差达6.58 mm，指出实现相机位姿与深度的全局一致性是提升临床应用的关键。
摘要：
从内窥镜视频生成准确的三维重建，为鼻窦解剖结构和手术结果的纵向无辐射分析提供了一条有前景的途径。目前已提出了多种单目重建方法，通过使用运动恢复结构（structure-from-motion）类算法检索相对相机位姿，并结合单目深度估计的融合，生成视觉上令人满意的三维解剖结构。然而，由于底层算法和内窥镜场景的复杂属性，重建流程可能表现不佳或意外失败。此外，获取医学数据带来了额外的挑战，给定量基准测试这些模型、理解失败案例以及识别影响其精度的关键组件带来了困难。在这项工作中，我们对一种用于鼻窦重建的自监督方法进行了定量分析，使用了配对光学追踪和从九个离体标本获取的高分辨率计算机断层扫描（CT）的内窥镜序列。我们的结果显示，生成的重建与解剖结构高度一致，重建与CT分割之间的平均点到网格误差为0.91 mm。然而，在与内窥镜追踪和导航相关的点到点匹配场景中，我们发现平均目标配准误差为6.58 mm。我们确定位姿和深度估计的不准确性对该误差的贡献相等，并且具有较短轨迹的局部一致序列生成更准确的重建。这些结果表明，实现相对相机位姿和估计深度与解剖结构之间的全局一致性至关重要。通过这样做，我们可以确保重建流程所有组件之间的适当协同作用，以获得更好的重建效果，从而促进这项创新技术的临床应用。
英文摘要：
Generating accurate 3D reconstructions from endoscopic video is a promising avenue for longitudinal radiation-free analysis of sinus anatomy and surgical outcomes. Several methods for monocular reconstruction have been proposed, yielding visually pleasant 3D anatomical structures by retrieving relative camera poses with structure-from-motion-type algorithms and fusion of monocular depth estimates. However, due to the complex properties of the underlying algorithms and endoscopic scenes, the reconstruction pipeline may perform poorly or fail unexpectedly. Further, acquiring medical data conveys additional challenges, presenting difficulties in quantitatively benchmarking these models, understanding failure cases, and identifying critical components that contribute to their precision. In this work, we perform a quantitative analysis of a self-supervised approach for sinus reconstruction using endoscopic sequences paired with optical tracking and high-resolution computed tomography acquired from nine ex-vivo specimens. Our results show that the generated reconstructions are in high agreement with the anatomy, yielding an average point-to-mesh error of 0.91 mm between reconstructions and CT segmentations. However, in a point-to-point matching scenario, relevant for endoscope tracking and navigation, we found average target registration errors of 6.58 mm. We identified that pose and depth estimation inaccuracies contribute equally to this error and that locally consistent sequences with shorter trajectories generate more accurate reconstructions. These results suggest that achieving global consistency between relative camera poses and estimated depths with the anatomy is essential. In doing so, we can ensure proper synergy between all components of the pipeline for improved reconstructions that will facilitate clinical application of this innovative technology.
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