背景:生命科学中成像技术的技术发展使得能够以增加的时间分辨率对活体样本进行三维记录。发育中生物的动态3D数据集允许对三维形态发生变化进行时间分辨的定量分析,但需要高效且可自动化的分析管道来处理由此产生的TB级图像数据。粒子图像测速(PIV)是一种强大且无分割的技术,适用于量化具有不同标记方案的数据集上的集体细胞迁移。本文介绍了使用Julia编程语言quickPIV实现高效的3DPIV包。我们的软件专注于优化CPU性能,并确保生物数据的PIV分析的鲁棒性。
结果:QuickPIV比openPIV中托管的Python实现快三倍,在2D和3D。我们的软件也比openPIV中最快的2DPIV包更快,用C++编写。我们的软件对合成数据的准确性评估与文献中描述的预期准确性一致。此外,通过将quickPIV应用于三个栗树胚胎发生的数据集,我们获得了矢量场,概括了原肠胚的迁移运动,在核和肌动蛋白标记的胚胎中。我们表明,归一化的平方误差互相关在检测不可分割的生物图像数据中的平移时特别准确。
结论:所提供的软件满足了生物学研究中对快速且开源的3DPIV软件包的需求。目前,quickPIV提供高效的2D和3DPIV分析,具有零归一化和归一化平方误差互相关,子像素/体素近似,和多通。后处理选项包括过滤和平均产生的矢量场,速度的提取,发散和集体地图,模拟伪轨迹,和单位转换。此外,我们的软件包括在Paraview中可视化3D矢量场的功能。
BACKGROUND: The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.
RESULTS: QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.
CONCLUSIONS: The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.