3D

A Python toolbox for registering / fusing / stitching large multi-view / multi-positioning image datasets in 2-3D.

FishFeats: napari plugin to perform quantification of multimodal labeling at the single-cell level in 3D tissues.

FishFeats allows to perform together in the same pipeline several analysis to quantify epithelia cells in 3D tissue, analysing cell morphology, nuclei, immuno-staining or RNA expression. The plugin allows flexibility to let the user choose the relevant step for a specific biological question.

This plugin is able to stitch an arbitrary collection or grid of images, it does not matter if it is 2d, 3d, 4d or 5d images as long as all images are of the same type. In contrast to the Pairwise Stitching of two images, this plugins will load (and potentially save) the images from/to harddisc.

This toolkit extracts Spherical Textures: Angular projections of 2D or 3D image objects with subsequent spherical harmonics analysis.

From the author summary: "We introduce a novel method to extract quantitative data from microscopy images by precisely measuring the distribution of intensities within objects in both 3D and 2D. This method is easily accessible through the object classification workflow of ilastik, provided the original image is segmented into separate objects. The method is specifically designed to analyze the convex region in objects, focusing on the variation in fluorescence intensity caused by differences in their shapes or patterns."

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The Pairwise Stitching first queries for two input images that you intend to stitch. They can contain rectangular ROIs which limit the search to those areas, however, the full images will be stitched together. Once you selected the input images it will show the actual dialog for the Pairwise Stitching.