DeconvolutionLab2: An open-source software for deconvolution microscopy.

By Daniel Sage, Laurène Donati, Ferréol Soulez, Denis Fortun, G. Schmit, Arne Seitz, Romain Guiet, Cédric Vonesch, Michael A. Unser. Methods, 2017

539 citations recorded by the source corpus.

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biological samples. Unfortunately, dealing with deconvolution tools is not straightforward. Among others, end users have to select the appropriate algorithm, calibration and parametrization, while potentially facing demanding computational tasks. To make deconvolution more accessible, we have developed a practical platform for deconvolution microscopy called DeconvolutionLab. Freely distributed, DeconvolutionLab hosts standard algorithms for 3D microscopy deconvolution and drives them through a user-oriented interface. In this paper, we take advantage of the release of DeconvolutionLab2 to provide a complete description of the software package and its built-in deconvolution algorithms. We examine several standard algorithms used in deconvolution microscopy, notably: Regularized inverse filter, Tikhonov regularization, Landweber, Tikhonov-Miller, Richardson-Lucy, and fast iterative shrinkage-thresholding. We evaluate these methods over large 3D microscopy images using simulated datasets and real experimental images. We distinguish the algorithms in terms of image quality, performance, usability and computational requirements. Our presentation is completed with a discussion of recent trends in deconvolution, inspired by the results of the Grand Challenge on deconvolution microscopy that was recently organized.

Subfields: Atomic and Molecular Physics, and Optics, Biophysics

View Paper: https://www.semanticscholar.org/paper/653d9149cb8e6ed82d67f9fa8fcb4b78cf39fff3

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Citations in the corpus

• A pipeline for multidimensional confocal analysis of mitochondrial morphology, function and dynamics in pancreatic β-cells (2019, methodology): “12 ImageJ/Fiji, deconvolution of 3D stacks is done using the DeconvolutionLab2 module (39) and if 323 desired, the 3D stack can be visualized using the “3D Viewer” or “Volume Viewer” functions.”