Department of Data Science, College of Computing, City University of Hong Kong, Hong Kong SAR, China.
Hefei National Laboratory for Physical Sciences at the Microscale and Department of Physics, University of Science and Technology of China, Hefei, Anhui, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae591.
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.
细胞跟踪是自动细胞活动监测中必不可少的功能。在实践中，处理方法在计算效率和准确性之间取得平衡，并在不同的细胞数据集上表现出强大的通用性，这是非常需要的。本文提出了一种用于细胞跟踪的中心度量全测试时自适应框架（CMTT-JTracker）。首先，设计了一种 CMTT 机制用于细胞图像的预分割，该机制能够在不进行额外训练的情况下，以不同的分辨率提取目标信息。接下来，开发了一个具有空间注意力机制的多任务学习网络，基于 CMTT 提取的特征同时实现检测和再识别任务。实验结果表明，CMTT-JTracker 与基准跟踪方法相比，具有显著的生物学和跟踪性能。在 Fluo-N2DH-SIM+上，它的多目标跟踪精度（MOTA）达到 0.894，在 PhC-C2DL-PSC 上，它的 MOTA 达到 0.850。实验结果进一步证实，CMTT 仅作为分割单元使用，在各种数据集上的表现优于 SOTA 分割基准，特别是在细胞密集的情况下表现出色。CMTT 在不同数据集上的 Dice 系数从 0.928 到 0.758 不等。
