In this work, we describe a method for large-scale 3D cell-tracking through a
segmentation selection approach. The proposed method is effective at tracking
cells across large microscopy datasets on two fronts: (i) It can solve problems
containing millions of segmentation instances in terabyte-scale 3D+t datasets;
(ii) It achieves competitive results with or without deep learning, which
requires 3D annotated data, that is scarce in the fluorescence microscopy
field. The proposed method computes cell tracks and segments using a hierarchy
of segmentation hypotheses and selects disjoint segments by maximizing the
overlap between adjacent frames. We show that this method achieves
state-of-the-art results in 3D images from the cell tracking challenge and has
a faster integer linear programming formulation. Moreover, our framework is
flexible and supports segmentations from off-the-shelf cell segmentation models
and can combine them into an ensemble that improves tracking. The code is
available this https URL

通过分割选择方法实现大规模三维细胞追踪，该方法在两个方面对大规模显微数据集中的细胞追踪问题具有有效性：（i）能够解决拥有数百万分割实例的 TB 级三维时间数据集的问题；（ii）能够在不依赖稀缺的三维标注数据的情况下与使用深度学习的方法达到竞争水平。该方法通过分割假设的层次结构计算细胞轨迹和分割，并通过最大化相邻帧之间的重叠来选择不相交的分割。证明该方法在细胞追踪挑战中实现了最先进的结果，并且拥有更快的整数线性规划公式。此外，我们的框架灵活性强，支持来自现成细胞分割模型的分割，并能够将它们组合成一个提高追踪效果的整体。代码可以在此链接中找到：https:// 此链接网址