SpindlesTracker: An Automatic and Low-Cost Labeled Workflow for Spindle Analysis
SpindlesTracker: An Automatic and Low-Cost Labeled Workflow for Spindle Analysis
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DOI：
10.1109/jbhi.2023.3281454
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发表时间：
2023-08-01
影响因子：
7.7
通讯作者：
Liu，Ji
中科院分区：
文献类型：
--
作者：
Li，Zhongzhong;Jian，Yanze;Liu，Ji
通过荧光显微镜定量分析有丝分裂中的纺锤体动力学需要在噪声图像序列中跟踪纺锤体的伸长。确定性方法使用典型的微管检测和跟踪方法，在纺锤体的复杂背景下表现不佳。此外，昂贵的数据标注成本也限制了机器学习在该领域的应用。在这里，我们提出了一种全自动、低成本的标记工作流，它有效地分析了延时图像的动态主轴机制，称为SpindlesTracker。在该工作流中，我们设计了一个名为YOLOX-SP的网络，该网络可以在箱级数据监控下准确地检测每个主轴的位置和端点。然后对算法SORT和MCP进行了优化，实现了主轴的跟踪和骨架化。由于没有公开可用的数据集，我们注释了完全从真实世界获得的S.pombeDataset，用于培训和评估。广泛的实验表明，SpindlesTracker在各个方面都取得了优异的性能，同时降低了60%的标签成本。具体来说，主轴检测的MAP达到84.1%，终点检测的准确率达到90%以上。此外，改进算法的跟踪精度提高了1.3%，跟踪精度提高了6.5%。统计结果还表明，纺锤体长度的平均误差在1μm以内。总之，SpindlesTracker对有丝分裂动力学机制的研究具有重要意义，并可以很容易地扩展到对其他丝状物体的分析。代码和数据集都在GitHub上发布。
Quantitative analysis of spindle dynamics in mitosis through fluorescence microscopy requires tracking spindle elongation in noisy image sequences. Deterministic methods, which use typical microtubule detection and tracking methods, perform poorly in the sophisticated background of spindles. In addition, the expensive data labeling cost also limits the application of machine learning in this field. Here we present a fully automatic and low-cost labeled workflow that efficiently analyzes the dynamic spindle mechanism of time-lapse images, called SpindlesTracker. In this workflow, we design a network named YOLOX-SP which can accurately detect the location and endpoint of each spindle under box-level data supervision. We then optimize the algorithm SORT and MCP for spindle's tracking and skeletonization. As there was no publicly available dataset, we annotated aS.pombedataset that was entirely acquired from the real world for both training and evaluation. Extensive experiments demonstrate that SpindlesTracker achieves excellent performance in all aspects, while reducing label costs by 60%. Specifically, it achieves 84.1% mAP in spindle detection and over 90% accuracy in endpoint detection. Furthermore, the improved algorithm enhances tracking accuracy by 1.3% and tracking precision by 6.5%. Statistical results also indicate that the mean error of spindle length is within 1 μm. In summary, SpindlesTracker holds significant implications for the study of mitotic dynamic mechanisms and can be readily extended to the analysis of other filamentous objects. The code and the dataset are both released on GitHub.