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.

通过定量分析，我们使用自监督方法对内窥镜定位进行了定量分析，采用与高分辨率计算机断层扫描相结合的内窥镜序列和光学跟踪获取的 9 个离体标本的结果显示，所生成的重建与解剖结构高度一致，重建与 CT 分割间的平均点对网格误差为 0.91 毫米，然而，在点对点匹配场景中，即对内窥镜跟踪和导航至关重要的情况下，我们发现平均目标注册误差为 6.58 毫米。我们发现姿态与深度估计的不准确性对此误差贡献相等，并且具有更短轨迹的局部一致性序列生成更准确的重建。因此，为了改进重建并促进临床应用，实现相对相机姿态和估计深度与解剖学的全局一致性至关重要。

单目内窥镜视频中鼻窦解剖结构的稠密三维重建的定量评估

A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy  from Monocular Endoscopic Video

Deepfakes raised serious concerns on the authenticity of visual contents.
Prior works revealed the possibility to disrupt deepfakes by adding adversarial
perturbations to the source data, but we argue that the threat has not been
eliminated yet. This paper presents MagDR, a mask-guided detection and
reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR
starts with a detection module that defines a few criteria to judge the
abnormality of the output of deepfakes, and then uses it to guide a learnable
reconstruction procedure. Adaptive masks are extracted to capture the change in
local facial regions. In experiments, MagDR defends three main tasks of
deepfakes, and the learned reconstruction pipeline transfers across input data,
showing promising performance in defending both black-box and white-box
attacks.

本文提出了一个名为 MagDR 的方案，该方案基于掩码引导检测和重建流程，以对抗 Deepfakes 的对抗攻击。利用检测模块来定义 Deepfakes 的输出异常性，提取适应性掩码来捕获局部面部区域的变化，并通过学习重构流程实现防御。MagDR 在防御黑盒和白盒攻击方面表现出有希望的性能。