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Mechanical strain and stress play a major role in biological processes suchas wound healing or morphogenesis. To assess this role quantitatively, fixed orlive images of tissues are acquired at a cellular precision in large fields ofviews. To exploit these data, large numbers of cells have to be analyzed toextract cell shape anisotropy and cell size. Most frequently, this is performedthrough detailed individual cell contour determination, using so-calledsegmentation computer programs, complemented if necessary by manual detectionand error corrections. However, a coarse grained and faster technique can berecommended in at least three situations. First, when detailed information onindividual cell contours is not required, for instance in studies which requireonly coarse-grained average information on cell anisotropy. Second, as anexploratory step to determine whether full segmentation can be potentiallyuseful. Third, when segmentation is too difficult, for instance due to poorimage quality or too large a cell number. We developed a user-friendly, Fouriertransform-based image analysis pipeline. It is fast (typically $10^4$ cells perminute with a current laptop computer) and suitable for time, space or ensembleaverages. We validate it on one set of artificial images and on two sets offully segmented images, one from a Drosophila pupa and the other from a chickenembryo; the pipeline results are robust. Perspectives include \textit{in vitro}tissues, non-biological cellular patterns such as foams, and $xyz$ stacks.

Fast determination of coarse grained cell anisotropy and size in epithelial tissue images using Fourier transform