Fedorchuk Khelina, Russell Sarah M, Zibaei Kajal, Yassin Mohammed, Hicks Damien G
Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Immune Signalling Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
PLoS One. 2025 Feb 10;20(2):e0315947. doi: 10.1371/journal.pone.0315947. eCollection 2025.
Time-lapse microscopy has long been used to record cell lineage trees. Successful construction of a lineage tree requires tracking and preserving the identity of multiple cells across many images. If a single cell is misidentified the identity of all its progeny will be corrupted and inferences about heritability may be incorrect. Successfully avoiding such identity errors is challenging, however, when studying highly-motile cells such as T lymphocytes which readily change shape from one image to the next. To address this problem, we developed DeepKymoTracker, a pipeline for combined tracking and segmentation. Central to DeepKymoTracker is the use of a seed, a marker for each cell which transmits information about cell position and identity between sets of images during tracking, as well as between tracking and segmentation steps. The seed allows a 3D convolutional neural network (CNN) to detect and associate cells across several consecutive images in an integrated way, reducing the risk of a single poor image corrupting cell identity. DeepKymoTracker was trained extensively on synthetic and experimental T lymphocyte images. It was benchmarked against five publicly available, automatic analysis tools and outperformed them in almost all respects. The software is written in pure Python and is freely available. We suggest this tool is particularly suited to the tracking of cells in suspension, whose fast motion makes lineage assembly particularly difficult.
延时显微镜技术长期以来一直用于记录细胞谱系树。成功构建谱系树需要在多张图像中跟踪并保留多个细胞的身份。如果单个细胞被错误识别，其所有后代的身份都会被破坏，关于遗传力的推断可能会不正确。然而，在研究诸如T淋巴细胞这类高度活跃的细胞时，成功避免此类身份错误具有挑战性，因为T淋巴细胞在相邻图像之间很容易改变形状。为了解决这个问题，我们开发了DeepKymoTracker，这是一种用于联合跟踪和分割的流程。DeepKymoTracker的核心是使用种子，即每个细胞的一个标记，它在跟踪过程中以及在跟踪和分割步骤之间传递有关细胞位置和身份的信息。该种子使三维卷积神经网络（CNN）能够以集成方式在几个连续图像中检测并关联细胞，降低了单个质量不佳的图像破坏细胞身份的风险。DeepKymoTracker在合成和实验性T淋巴细胞图像上进行了广泛训练。它与五个公开可用的自动分析工具进行了基准测试，并且在几乎所有方面都优于它们。该软件用纯Python编写，可免费获取。我们认为这个工具特别适合跟踪悬浮细胞，因为它们的快速运动使得谱系组装尤其困难。
