Accurate tracking of cellular and subcellular structures, along with their
dynamics, plays a pivotal role in understanding the underlying mechanisms of
biological systems. This paper presents a novel approach, ProGroTrack, that
combines the You Only Look Once (YOLO) and ByteTrack algorithms within the
detection-based tracking (DBT) framework to track intracellular protein
nanostructures. Focusing on iPAK4 protein fibers as a representative case
study, we conducted a comprehensive evaluation of YOLOv5 and YOLOv8 models,
revealing the superior performance of YOLOv5 on our dataset. Notably, YOLOv5x
achieved an impressive mAP50 of 0.839 and F-score of 0.819. To further optimize
detection capabilities, we incorporated semi-supervised learning for model
improvement, resulting in enhanced performances in all metrics. Subsequently,
we successfully applied our approach to track the growth behavior of iPAK4
protein fibers, revealing their two distinct growth phases consistent with a
previously reported kinetic model. This research showcases the promising
potential of our approach, extending beyond iPAK4 fibers. It also offers a
significant advancement in precise tracking of dynamic processes in live cells,
and fostering new avenues for biomedical research.

介绍了一种追踪细胞和亚细胞结构及其动态变化的新方法 ——ProGroTrack，它结合了 YOLO 和 ByteTrack 算法来追踪细胞内蛋白纳米结构，并在 iPAK4 蛋白纤维上进行了案例研究，验证了该技术的优越性，通过半监督学习进一步提高了检测性能，成功地应用于 iPAK4 蛋白纤维的生长行为跟踪，展示了该方法在精确追踪细胞动态过程方面的前景和应用潜力。

ProGroTrack: 深度学习辅助下的细胞内蛋白质生长动态跟踪

ProGroTrack: Deep Learning-Assisted Tracking of Intracellular Protein  Growth Dynamics

Due to its specificity, fluorescence microscopy (FM) has become a
quintessential imaging tool in cell biology. However, photobleaching,
phototoxicity, and related artifacts continue to limit FM's utility. Recently,
it has been shown that artificial intelligence (AI) can transform one form of
contrast into another. We present PICS, a combination of quantitative phase
imaging and AI, which provides information about unlabeled live cells with high
specificity. Our imaging system allows for automatic training, while inference
is built into the acquisition software and runs in real-time. Applying the
computed fluorescence maps back to the QPI data, we measured the growth of both
nuclei and cytoplasm independently, over many days, without loss of viability.
Using a QPI method that suppresses multiple scattering, we measured the dry
mass content of individual cell nuclei within spheroids. In its current
implementation, PICS offers a versatile quantitative technique for continuous
simultaneous monitoring of individual cellular components in biological
applications where long-term label-free imaging is desirable.

利用量化相位成像和人工智能技术结合的 PICS，可以提供高度特异性的未标记活细胞信息，并为长期无标签成像的生物应用提供了一种多功能定量技术。