寻找纳米奥蒂茨：用于冷冻电镜断层扫描的半监督体积可视化
Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography
AI总结：
本文提出半监督体积可视化方法，通过弱分割和深度学习分割结合，提升低信噪比冷冻电镜数据的可视化效果，实现噪声抑制和结构细节突出。
摘要：
冷冻电镜断层扫描（cryo-ET）是一种新的三维成像技术，具有前所未有的潜力来解析亚微米结构细节。然而，现有的体积可视化方法无法应对其极低的信噪比。为设计更强大的转移函数，我们提出利用软分割作为可视化中的显式组件。我们的技术实现基于半监督学习，结合两种分割算法的优势。一种弱分割算法能够有效地将稀疏的用户提供的标签传播到同一体积中的其他体素。该弱分割算法用于生成密集的伪标签。另一种强大的基于深度学习的分割算法可以学习这些伪标签，从而泛化到其他未见过的体积，而弱分割算法无法完全做到这一点。所提出的体积可视化使用基于深度学习的分割作为分割感知的转移函数设计组件。通过直方图分析可以自动建议适当的梯度参数。最后，我们的可视化使用无梯度的环境遮蔽着色来进一步抑制噪声的视觉存在，并突出所需的结构细节。我们技术实验中研究的cryo-ET数据基于最高质量的完整SARS-CoV-2病毒倾斜系列。我们的技术在非常嘈杂的体积可视化分析中对目标科学具有高度影响。
英文摘要：
Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, however, cannot cope with its very low signal-to-noise ratio. In order to design more powerful transfer functions, we propose to leverage soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning where we combine the advantages of two segmentation algorithms. A first weak segmentation algorithm provides good results for propagating sparse user provided labels to other voxels in the same volume. This weak segmentation algorithm is used to generate dense pseudo labels. A second powerful deep-learning based segmentation algorithm can learn from these pseudo labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses the deep-learning based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through histogram analysis. Finally, our visualization uses gradient-free ambient occlusion shading to further suppress visual presence of noise, and to give structural detail desired prominence. The cryo-ET data studied throughout our technical experiments is based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
