Abstract
BackgroundThe 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.ResultsQuickPIV 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.ConclusionsThe 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.
Highlights
The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions
In order to quantify collective cellular migration in dynamic 3D biological data sets, we developed quickPIV, a free and open-source particle image velocimetry (PIV) package that offers fast and robust 3D, as well as 2D, Particle image velocimetry (PIV) analyses
The accuracy evaluation of quickPIV quantitatively reproduces the expected accuracies described in the PIV literature, attesting the correctness of our PIV implementation [52,53,54,55]
Summary
The accuracy evaluation of quickPIV quantitatively reproduces the expected accuracies described in the PIV literature, attesting the correctness of our PIV implementation [52,53,54,55]. Our PIV analyses capture the coordinated condensation movement of the cells in the central and posterior regions, which will later give rise to the germband The agreement of the vector fields on the anterior pole of the embryo (which is non-segmentable in the actin signal, segmentable in the nuclear signal and exhibits high degrees of collective cell migration in both channels) indicates that PIV accuracies are independent of the segmentability of the input data sets, Figs. A quantitative comparison of the PIV vector fields between the nuclear and the actin stained volumes shows a high degree of similarity This is illustrated in the scatter plot shown, exhibiting a high density in the area of large dot products and small Euclidean errors. The similarity of the actin and nuclear vector fields in highly dense non-segmentable regions further underlines the robustness of quickPIV regardless of the labeling scheme of the data sets
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