  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 https://github.com/royerlab/ultrack.
