Cell tracking is an omnipresent image analysis task in live-cell microscopy. It is similar to multiple object tracking (MOT), however, each frame contains hundreds of similar-looking objects that can divide, making it a challenging problem. Current state-of-the-art approaches follow the tracking-by-detection paradigm, i.e. first all cells are detected per frame and successively linked in a second step to form biologically consistent cell tracks. Linking is commonly solved via discrete optimization methods, which require manual tuning of hyperparameters for each dataset and are therefore cumbersome to use in practice. Here we propose Trackastra, a general purpose cell tracking approach that uses a simple transformer architecture to directly learn pairwise associations of cells within a temporal window from annotated data. Importantly, unlike existing transformer-based MOT pipelines, our learning architecture also accounts for dividing objects such as cells and allows for accurate tracking even with simple greedy linking, thus making strides towards removing the requirement for a complex linking step. The proposed architecture operates on the full spatio-temporal context of detections within a time window by avoiding the computational burden of processing dense images. We show that our tracking approach performs on par with or better than highly tuned state-of-the-art cell tracking algorithms for various biological datasets, such as bacteria, cell cultures and fluorescent particles. We provide code at https://github.com/weigertlab/trackastra.

翻译：细胞追踪是活细胞显微成像中普遍存在的图像分析任务。该任务类似于多目标追踪（MOT），但每一帧图像包含数百个外观相似且可能发生分裂的物体，使其成为一个具有挑战性的问题。当前最先进的方法遵循"检测后追踪"范式，即首先逐帧检测所有细胞，随后在第二步中将它们关联起来以形成生物学上一致的细胞轨迹。关联步骤通常通过离散优化方法解决，这些方法需要对每个数据集手动调整超参数，因此在实践中使用起来较为繁琐。本文提出Trackastra，一种通用的细胞追踪方法，它使用简单的Transformer架构直接从标注数据中学习时间窗口内细胞的成对关联关系。重要的是，与现有的基于Transformer的MOT流程不同，我们的学习架构还考虑了细胞等分裂物体，并且即使使用简单的贪心关联也能实现精确追踪，从而在消除复杂关联步骤的需求方面取得了进展。所提出的架构通过避免处理密集图像的计算负担，在时间窗口内检测结果的完整时空上下文中进行操作。我们证明，对于各种生物数据集（如细菌、细胞培养物和荧光颗粒），我们的追踪方法性能与经过高度调优的最先进细胞追踪算法相当或更优。代码发布于 https://github.com/weigertlab/trackastra。