Imagej piv tutorial4/20/2024 ![]() ![]() This phenomenon is generally known as collective cell migration, and it plays important roles in developmental processes, such as gastrulation or neural crest migration, as well as in wound closure and cancer invasion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.Ĭellular migration in multi-cellular organisms often involves tissues or groups of cells that maintain stable or transient cell-cell contacts to preserve tissue integrity, sustain spatial patterning, or to enable the relocation of non-motile cells. 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. 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. The presented software addresses the need for a fast and open-source 3D PIV package in biological research. ![]() We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data. ![]() 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. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. 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. 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. Regarding the result of another quantitative investigation, it is found that the SME method is applicable to the laminar–turbulent transition flow because the turbulent structures in different sizes can be simultaneously captured by the SME method.The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Additionally, α is strongly related to an image contrast gradient appearing in a schlieren image, and the strong image contrast gradient leads to the better weight parameter α in a wide range. The experimental results showed that a weight parameter α between governing equation and a constraint condition is sensitive to a velocity estimation accuracy. In this study, we conducted two quantitative investigations: (1) the sensitivity in an estimation accuracy with the user-defined weight parameters in the SME method and (2) the performances of the SME method for a laminar–turbulent transition flow in which a velocity field would be difficult to be estimated by a standard schlieren image velocimetry technique driven by a block-matching algorithm. ![]() This seedless velocimetry technique is useful for flow situations where the seeding particles are difficult to supply or identify. A schlieren motion estimation (SME) method is a physical-based schlieren image velocimetry involving both schlieren luminance characteristic equation and continuity equation. ![]()
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