发表机构
Indiana University Bloomington（印第安纳大学布卢明顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对稀疏机器人活细胞成像中细胞谱系记录断裂的问题，提出PSF感知的4D高斯泼溅模型，按需重建缺失体积，在两个挑战赛序列上实现最高细胞核保真度。
AI 中文摘要
一台观察活细胞的机器人显微镜无法像它希望的那样频繁地观察。它获取的每个体积都会消耗标本无法收回的光子，以及本应属于其他孔的时间。这种平台存在的目的是生成单个细胞随时间变化的记录：从一个体积到下一个体积，哪个细胞是哪个，以及哪个细胞分裂成了哪两个。稀疏采样恰恰在最关键的地方破坏了这种记录，而问题在于采集计划而非分析软件。我们通过重建来填补这些空白，将4D高斯模型拟合到硬件能够负担的任何体积上。该模型是一团发光斑点的云，每个斑点携带位置、形状和寿命。由于在时间上是连续的，它可以按需渲染任何缺失的体积，从而将机器人分析的频率与其能够负担的观察频率解耦。显微镜的点扩散函数是从数据中测量的，而不是从采集元数据中继承或留给优化器处理，因为元数据会使其膨胀，而优化器根本无法恢复它：围绕较小斑点的更宽模糊同样能拟合图像。每个斑点的寿命以帧为单位存储，而不是作为记录的分数，因此它在任何序列上都表示固定的持续时间。未测量的时间步长由3D U-Net以粗尺度进行监督，该网络直接预测中间体积，并且不估计运动场，因为分裂的细胞变成两个，没有运动能描述这一点。在两个细胞追踪挑战赛序列上，即秀丽隐杆线虫胚胎和中国仓鼠卵巢（CHO）细胞，以每个细胞核的保真度进行评分，我们的重建在距采集帧的任何距离处都保持最高的细胞核保真度，在间隙中具有最平坦的衰减，并且最好地恢复了从未显示过的焦平面，在间隙中具有优雅的退化。
英文摘要
A robotic microscope watching living cells cannot afford to look as often as it would like. Every volume it acquires costs photons the specimen does not get back, and time owed to other wells. What such a platform exists to produce is a record of individual cells through time: which cell is which from one volume to the next, and which cell divided into which two. Sampling sparsely breaks that record exactly where it matters, and the fault lies in the acquisition schedule rather than in the analysis software. We fill the gaps by reconstructing them, fitting a 4D Gaussian model to whatever volumes the hardware could afford. The model is a cloud of light-emitting blobs, each carrying a position, a shape and a lifetime. Being continuous in time, it renders any missing volume on demand, decoupling how often the robot analyses from how often it can afford to look. The microscope's point-spread function is measured from the data rather than inherited from acquisition metadata or left to the optimizer, because metadata inflates it and the optimizer cannot recover it at all: a wider blur around a smaller blob fits the images equally well. Each blob's lifetime is stored in frames rather than as a fraction of the recording, so that it denotes a fixed duration on any sequence. Unmeasured timesteps are supervised at coarse scale by a 3D U-Net that predicts the intermediate volume directly and estimates no motion field, since a dividing cell becomes two and no motion describes that. On two Cell Tracking Challenge sequences, a C. elegans embryo and a Chinese Hamster Ovarian (CHO), with fidelity scored per cell nucleus, our reconstruction holds the highest nucleus fidelity at every distance from an acquired frame, has the flattest decay across the gap, and best recovers focal planes it was never shown with graceful degradation across the gap.
