LocalZProjectorandDeProj: A toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images

Abstract

BackgroundQuantitative imaging of epithelial tissues prompts for bioimage analysis tools that are widely applicable and accurate. In the case of imaging 3D tissues, a common post-processing step consists in projecting the acquired 3D volume on a 2D plane mapping the tissue surface. Indeed, while segmenting the tissue cells is amenable on 2D projections, it is still very difficult and cumbersome in 3D. However, for many specimen and models used in Developmental and Cell Biology, the complex content of the image volume surrounding the epithelium in a tissue often reduces the visibility of the biological object in the projection, compromising its subsequent analysis. In addition, the projection will distort the geometry of the tissue and can lead to strong artifacts in the morphology measurement.ResultsHere we introduce DProj a user-friendly tool-box built to robustly project epithelia on their 2D surface from 3D volumes, and to produce accurate morphology measurement corrected for the projection distortion, even for very curved tissues. DProj is built upon two components. LocalZProjector is a user-friendly and configurable Fiji plugin that generates 2D projections and height-maps from potentially large 3D stacks (larger than 40 GB per time-point) by only incorporating the signal of interest, despite a possibly complex image content. DeProj is a MATLAB tool that generates correct morphology measurements by combining the height-map output (such as the one offered by LocalZProjector) and the results of the cell segmentation on the 2D projection. In this paper we demonstrate DProj effectiveness over a wide range of different biological samples. We then compare its performance and accuracy against similar existing tools.ConclusionsWe find that LocalZProjector performs well even in situations where the volume to project contains spurious structures. We show that it can process large images without a pre-processing step. We study the impact of geometrical distortions on morphological measurements induced by the projection. We measured very large distortions which are then corrected by DeProj, providing accurate outputs.

Robustness of epithelial sealing is an emerging property of local ERK feedback driven by cell elimination

“…Nuclear signal was then obtained by projecting maximum of intensity on 7 μm (7 slides) around a focal point which was located 6 μm basal to adherens junctions. This procedure was either applied using a custom Matlab routine ( Moreno et al., 2019 ) or through the Fiji plugin LocalZprojector ( Herbert et al., 2021 ).…”

DeepProjection: Rapid and structure-specific projections of tissue sheets embedded in 3D microscopy stacks using deep learning

“…To demonstrate the capabilities of DP, we compared its performance with simple MIP and three published algorithms FastSME (FSME) 15 , Local Z Projector (LZP) 6 and CSBDeep (CSBD) 8 . We trained CSBDeep with the MIP of the masked stacks of the DP training data as GT (using default parameters, 200 epochs with learning rate 4e-5).…”

“…The extracted 2D manifolds are therefore not smooth. More sophisticated approaches can be classified into three categories: (i) smoothing of height maps derived by MIP 3 , (ii) ranking of z-slices by visual pattern recognition of targeted tissue structures by edge filters 4 , Fourier transforms or wavelet transforms 5 , or (iii) evaluating mean and variance of intensity distributions in the neighborhood of each pixel 6 . All these algorithms only perform well when the target structures are bright and clearly distinguishable from background noise.…”

Myosin waves and a mechanical asymmetry guide the oscillatory migration of Drosophila cardiac progenitors

“…Maximum intensity projections were used for analysis in the heart. In the epidermis, Z-stacks were projected using a local Z projector to correct for the curvature of the embryo (Herbert et al, 2021). To image the dynamics along the entire length of the heart, 3 overlapping positions along the embryo were imaged as above and maximum intensity projections were stitched using a feathering blending algorithm, where the weighting coefficients at the stitching seam between two input images were calculated based on the distances of each input from the seam (Uyttendaele et al, 2001).…”