Cell Tracking According to Biological Needs-Strong Mitosis-Aware Multi- Hypothesis Tracker With Aleatoric Uncertainty

Cell tracking and segmentation enable biologists to extract insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term consistency and an inability to correctly reconstruct lineage trees. To address this issue, we introduce a novel assignment strategy consisting of two key components. First, we propose an uncertainty estimation technique for motion estimation frameworks. This method relaxes single-point motion representations into probabilistic spatial densities using problem-specific test-time augmentations. Second, we leverage these spatial densities to define a novel mitosis-aware assignment problem formulation. This formulation allows multi-hypothesis trackers to model cell divisions and resolve false associations and mitosis detections based on long-term conflicts. Our framework integrates explicit biological knowledge into assignment costs and combines it with learned representations derived from spatial densities. We evaluate our approach on nine competitive datasets and demonstrate that it substantially outperforms the current state-of-the-art on biologically inspired metrics, achieving improvements by a factor of approximately six and providing new insights into the behavior of motion estimation uncertainty.