ArticleBMC bioinformatics2021
QuickPIV: Efficient 3D particle image velocimetry software applied to quantifying cellular migration during embryogenesis.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 8 citations in OpenAlex.
- Sixteen isotropic 3D fluorescence live-imaging datasets of gastrulation in the red flour beetle Tribolium castaneum (Coleoptera: Tenebrionidae).Scientific data · 2026Article
- Mapping cell dynamics in human ex vivo hair follicles suggests pulling mechanism of hair growth.Nature communications · 2025Article
- Ultrack: pushing the limits of cell tracking across biological scales.Nature methods · 2025Article
- Spatiotemporal segmentation of contraction waves in the extra-embryonic membranes of the red flour beetle.BMC bioinformatics · 2025Article
- Imaging actin organisation and dynamics in 3D.Journal of cell science · 2024Article
- Cell counting and velocity algorithms for hydrodynamic study of unsteady biological flows in micro-channels.Biomicrofluidics · 2023Article
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6 authors at 2 institutions in 1 country.
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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.
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