不完美的恢复：存在动态定位蛋白质的细胞分割
Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
摘要：
在生物医学数据上部署现成的分割网络已经成为常见的做法，但是如果图像序列中的感兴趣结构只是暂时可见，现有的逐帧方法就会失败。在本文中，我们提供了一个解决方案，通过时间的不完美的数据分割的时间传播和不确定性估计的基础上。我们将不确定性估计集成到Mask R-CNN网络中，并将运动校正的分割掩码从具有低不确定性的帧传播到具有高不确定性的帧，以处理用于分割的信号的暂时丢失。我们证明了这种方法的价值超过逐帧分割和定期的时间传播的数据从人胚肾（HEK 293 T）细胞瞬时转染的荧光蛋白，随着时间的推移，移动进出细胞核。本文提出的方法将使旨在了解分子和细胞功能的显微镜实验成为可能。
英文摘要：
Deploying off-the-shelf segmentation networks on biomedical data has become common practice, yet if structures of interest in an image sequence are visible only temporarily, existing frame-by-frame methods fail. In this paper, we provide a solution to segmentation of imperfect data through time based on temporal propagation and uncertainty estimation. We integrate uncertainty estimation into Mask R-CNN network and propagate motion-corrected segmentation masks from frames with low uncertainty to those frames with high uncertainty to handle temporary loss of signal for segmentation. We demonstrate the value of this approach over frame-by-frame segmentation and regular temporal propagation on data from human embryonic kidney (HEK293T) cells transiently transfected with a fluorescent protein that moves in and out of the nucleus over time. The method presented here will empower microscopic experiments aimed at understanding molecular and cellular function.
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。
