RedLionfish
RedLionfish is a package which is useful for removing optical artefacts in 3D microscopy data, leading to clearer and sharper images.
The figure below shows an example of experimental light microscopy of fluorescent beads. The image on the left is the raw data with fluorescent beads shown in green and appearing elongated and fuzzy around the edges (rather than spherical as expected) due to artefacts introduced by the light microscope. Because we know the expected shape of the fluorescent beads, we can use the 3D point spread function (PSF) to deconvolute the data; and this can be done efficiently due to the accelerated implementation found in the RedLionfish plugin, resulting in the image on the right, where beads appear more point-like and biological features appear significantly clearer. Depending on the GPU resources used, this process can take less than a minute, giving researchers a more accurate real-time view of their fluorescently marked features during an experiment (e.g., when milling during lamellae preparation for downstream cryogenic TEM).
The package allows for fast GPU/CPU accelerated Richardson-Lucy deconvolution of 3D optical images. RedLionfish was chosen for the name of this program so that its initials (RL) are the same as Richardson-Lucy who originally developed the algorithm.
This software is available as a plugin for napari (a 3D data visualization application) and it has been included in 3DCT as a data processing tool for correlative microscopy.
The open-source code for the RedLionfish plugin can be found here: https://github.com/rosalindfranklininstitute/RedLionfish
RedLionfish can be installed using PyPi or using napari’s installation interface.
https://pypi.org/project/RedLionfish/
Left, experimental volumetric light microscopy data of cryo-frozen fluorescent beads embedded with Hela cells. Right, Same volumetric data as in right, after applying RedLlionfish convolution algorithm with a suitable point-spread-function (PSF) and for 10 iterations. Resolution of beads and cell is clearly improved.
Project Leadership
Mark Basham
Michele Darrow
Project Members at the Franklin
Neville Yee
Maud Dumoux
Casper Berger
Funded by
Wellcome Trust (Electrifying Life Sciences)