Cell tracking in low-frame-rate video based on displacement prediction

基于位移预测的低帧率视频中的细胞跟踪

• 批准号: 10648570
• 负责人: Federico Iuricich
• 金额: $ 20.41万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10648570
• 关键词: A549; Address; Affect; Air; Algorithms; Area; Biochemistry; Bioinformatics; Biological; Biology; Cell Nucleus; Cells; Cellular biology; Cessation of life; Classification; Clover; Consumption; Data; Data Set; Developmental Biology; Drops; Environment; Equipment; Evaluation; Event; Formulation; Future; Genetic; HL60; Health; Hour; Image; Learning; Link; Locomotion; MCF10A cells; MDA MB 231; Machine Learning; Mammalian Cell; Manuals; Measures; Medicine; Membrane; Methods; Microscope; Morphologic artifacts; Morphology; Movement; Performance; Photobleaching; Phototoxicity; Preparation; Regenerative Medicine; Research; Research Personnel; Resources; Science; Speed; Techniques; Temperature; Testing; Time; Training; Visualization; Work; cell analyzer; cell motility; cell type; cellular imaging; cost; cytate; cytotoxicity; deep learning; deep neural network; design; drug discovery; improved; innovation; interest; light transmission; live cell imaging; neural network; novel strategies; recurrent neural network; tool

Project Summary Tracking living cells in video sequences is a fundamental task in many fields of science, including biochemistry, bioinformatics, cell biology, and genetics. Manually linking cells is extremely time-consuming and not feasible in large-scale analysis. Automatic approaches can compute cell links by measuring how close two instances of a cell are, or how similar they look. These techniques work well with video acquired at a relatively high frame rate, but, unfortunately, acquiring images at high frame rates affects cells negatively. Too frequent imaging not only causes phototoxicity, leading to experimental artifacts, but also photobleaching, leading to the inability to measure quantities of interest over time. In addition, during image acquisition, the environment temperature and air quality are typically less controlled, which could also contribute to cytotoxicity. Moreover, when performing high- throughput live-cell imaging, the lower the acquisition rate, the more cells/plates can be imaged, and, consequently, the more experimental treatments can be applied and studied. If reducing the acquisition rate is beneficial for all these reasons, it severely affects the accuracy of cell tracking algorithms. To this end, we propose a new class of cell tracking approaches based on cell movement predictions. Instead of comparing cells based on their similarity, we propose to predict where every cell will move in the next frame. This will allow for searching the occurrence of such cells, even if the next frame was acquired after an extended period. The new approach will be investigated using a newly generated dataset for low frame rate cell tracking (Aim 1). Cell displacement will be predicted by using a new Recurrent Neural Network designed for the task (Aim 2). Cell tracking algorithms will be defined re-evaluating existing approaches under low-frame rate constraints when using cell displacement information (Aim 3). While current approaches require image acquisition to occur at least every 5-15 minutes, we will investigate the feasibility of cell tracking on images acquired at intervals of up to 2 hours. If successful, our research will allow to accurately track cells in low frame rate video sequences without the need for specialized tools or equipment.

项目摘要 在视频序列中跟踪活细胞是许多科学领域中的基本任务， 包括生物化学、生物信息学、细胞生物学和遗传学。手动链接单元格是 非常耗时，在大规模分析中不可行。自动进近可以 通过测量一个单元的两个实例有多接近或它们看起来有多相似来计算单元链接。 这些技术对于以相对高的帧速率获取的视频工作良好，但是， 不幸的是，以高帧速率获取图像会对细胞产生负面影响。过于频繁 成像不仅引起光毒性，导致实验伪影，而且 光漂白，导致不能随时间测量感兴趣的量。此外，本发明还提供了一种方法， 在图像采集期间，环境温度和空气质量通常低于 控制，这也可能有助于细胞毒性。此外，当高性能- 通过活细胞成像，采集速率越低，可以成像的细胞/板越多， 因此，可以应用和研究更多的实验性治疗。 如果由于所有这些原因降低采集速率是有益的，则它严重影响精度 细胞追踪算法为此，我们提出了一类新的细胞跟踪方法， 细胞运动预测。我们建议， 来预测每个细胞在下一帧中的移动位置。这将允许搜索 即使下一帧是在延长的时间段之后获取的，也不会出现这种细胞。的 一种新的方法将使用新生成的低帧速率单元数据集进行研究 跟踪（目标1）。将使用一种新的递归神经网络预测单元位移 目标2（Aim 2）细胞跟踪算法将被重新定义， 当使用单元位移信息时在低帧速率约束下的方法（Aim （3）第三章。 虽然目前的方法要求至少每5-15分钟进行一次图像采集，但我们 将研究在间隔长达2小时的图像上进行细胞跟踪的可行性。如果 如果成功，我们的研究将允许在低帧速率视频序列中准确地跟踪细胞 而不需要专门的工具或设备。