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The #NeuroRigBuilder Team

Novel methods for flourescence image processing
https://www.neurorigbuilder.com #NeuroRigBuilder


Image deconvolution and computational fusion of multiple views of the same sample could be very expansive or time-consuming processes. Hari Shroff and his colleagues have published new tools which can accelerate multiview image fusion and deconvolution as well. They claim these novel tools can increase speed up to ten-fold to several thousand-fold. They showed nice examples of superresolution, large brain tissue and various biological samples.

Their software is also freely available and maintained through GitHub. The software includes four sets of programs for implementing (1) WB deconvolution on a variety of different microscopes; (2) rapid registration of two volumetric images, for example, for subsequent WB deconvolution; (3) registration and deconvolution of large cleared-tissue datasets, imaged with diSPIM; and (4) our convolutional neural network (DenseDeconNet) for resolution recovery. Programs run in MATLAB except for DenseDeconNet, which is written in Python.

Source: Guo, M., Li, Y., Su, Y. et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nat Biotechnol (2020).

For more information please visit: https://www.nature.com/articles/s41587-020-0560-x



Millimeter-scale cleared tissue of a mouse brain.

All images shown are for illustration purpose only. See details in Terms.
https://www.neurorigbuilder.com #NeuroRigBuilder