Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps
This addresses the problem of efficient and accurate cell tracking for researchers in fluorescence microscopy, where annotated data is scarce, though it is incremental as it builds on existing segmentation and tracking approaches.
The authors tackled large-scale 3D cell tracking in microscopy by proposing a segmentation selection method that handles millions of instances in terabyte-scale datasets, achieving state-of-the-art results in the cell tracking challenge with a faster integer linear programming formulation.
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.