CMTT-JTracker: a fully test-time adaptive framework serving automated cell lineage construction

Cell tracking is an essential function needed in automated cellular activity monitoring. In practice, processing methods striking a balance between computational efficiency and accuracy as well as demonstrating robust generalizability across diverse cell datasets are highly desired. This paper develops a central-metric fully test-time adaptive framework for cell tracking (CMTT-JTracker). Firstly, a CMTT mechanism is designed for the pre-segmentation of cell images, which enables extracting target information at different resolutions without additional training. Next, a multi-task learning network with the spatial attention scheme is developed to simultaneously realize detection and re-identification tasks based on features extracted by CMTT. Experimental results demonstrate that the CMTT-JTracker exhibits remarkable biological and tracking performance compared with benchmarking tracking methods. It achieves a multiple object tracking accuracy (MOTA) of 0.894 on Fluo-N2DH-SIM+ and a MOTA of 0.850 on PhC-C2DL-PSC. Experimental results further confirm that the CMTT applied solely as a segmentation unit outperforms the SOTA segmentation benchmarks on various datasets, particularly excelling in scenarios with dense cells. The Dice coefficients of the CMTT range from a high of 0.928 to a low of 0.758 across different datasets. © The Author(s) 2024. Published by Oxford University Press.