Cell tracking and segmentation assist biologists in extracting 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.
To address this issue, we introduce an uncertainty estimation technique for
neural tracking-by-regression frameworks and incorporate it into our novel
extended Poisson multi-Bernoulli mixture tracker. Our uncertainty estimation
identifies uncertain associations within high-performing tracking-by-regression
methods using problem-specific test-time augmentations. Leveraging this
uncertainty, along with a novel mitosis-aware assignment problem formulation,
our tracker resolves false associations and mitosis detections stemming from
long-term conflicts. We evaluate our approach on nine competitive datasets and
demonstrate that it outperforms the current state-of-the-art on biologically
relevant metrics substantially, achieving improvements by a factor of
approximately $5.75$. Furthermore, we uncover new insights into the behavior of
tracking-by-regression uncertainty.

细胞追踪和分割是从大规模显微镜时间序列数据中提取洞见所必需的。本文引入了一种不确定性估计技术，将其整合到我们的新颖的扩展 Poisson 多伯努利混合追踪器中，以解决当前追踪方法长期一致性不足的问题。我们的方法在九个竞争数据集上评估，证明在生物相关指标上表现优于当前的最新技术，并实现了约 5.75 倍的改进。此外，我们还揭示了关于追踪 - by-regression 不确定性行为的新见解。