PreMosa: Extracting 2D surfaces from 3D microscopy mosaics. - Institut Pasteur Access content directly
Journal Articles Bioinformatics Year : 2017

PreMosa: Extracting 2D surfaces from 3D microscopy mosaics.


A significant focus of biological research is to understand the development, organization and function of tissues. A particularly productive area of study is on single layer epithelial tissues in which the adherence junctions of cells form a 2D manifold that is fluorescently labeled. Given the size of the tissue, a microscope must collect a mosaic of overlapping 3D stacks encompassing the stained surface. Downstream interpretation is greatly simplified by preprocessing such a dataset as follows: (a) extracting and mapping the stained manifold in each stack into a single 2D projection plane, (b) correcting uneven illumination artifacts, (c) stitching the mosaic planes into a single, large 2D image, and (d) adjusting the contrast.
We have developed PreMosa, an efficient, fully automatic pipeline to perform the four preprocessing tasks above resulting in a single 2D image of the stained manifold across which contrast is optimized and illumination is even. Notable features are as follows. First, the 2D projection step employs a specially developed algorithm that actually finds the manifold in the stack based on maximizing contrast, intensity and smoothness. Second, the projection step comes first, implying all subsequent tasks are more rapidly solved in 2D. And last, the mosaic melding employs an algorithm that globally adjusts contrasts amongst the 2D tiles so as to produce a seamless, high-contrast image. We conclude with an evaluation using ground-truth datasets and present results on datasets from Drosophila melanogaster wings and Schmidtae mediterranea ciliary components.
PreMosa is available under,

Dates and versions

pasteur-01545824 , version 1 (23-06-2017)



Corinna Blasse, Stephan Saalfeld, Raphael Etournay, Andreas Sagner, Suzanne Eaton, et al.. PreMosa: Extracting 2D surfaces from 3D microscopy mosaics.. Bioinformatics, 2017, pp.Bioinformatics btx195. ⟨10.1093/bioinformatics/btx195⟩. ⟨pasteur-01545824⟩


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