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
this https URL

利用简单的 Transformer 架构直接学习细胞在时间窗口内的成对关联，从而提出一种通用的细胞跟踪方法，该方法能够在各种生物数据集（如细菌、细胞培养和荧光颗粒等）中表现与优化过的最先进细胞跟踪算法相媲美或更好，且能够避免复杂的关联步骤。