DeconvolutionLab2: An open-source software for deconvolution microscopy

Abstract

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.

Characterization of Trichuris muris secreted proteins and extracellular vesicles provides new insights into host–parasite communication

“…The whole experiment was repeated to perform laser scanning confocal imaging on a 780 NLO microscope (Zeiss). Confocal image deconvolution was performed in ImageJ using the plugins “Diffraction PSF 3D” for PSF calculation and “DeconvolutionLab” with the Richardson–Lucy algorithm for 3D deconvolution and Tikhonov–Miller algorithm for 2D deconvolution [23]. …”

All-reflective multiphoton microscope

“…5(a). An experimental Point Spread Function (PSF) was calculated from the image of the beads using deconvolution [29,30] (Fig. 5(b)).…”

E-cadherin expression pattern during zebrafish embryonic epidermis development

“…Quantification of E-cadh fluorescence intensity was carried throughout the epidermis bilayer (~ 6 μm) in calibrated 3D-ROIs set at 2500 µm 2 × 0.33 µm × 20 slices (16500 µm 3 ). First, deconvolution was performed on individual 3D-ROI by applying Richardson-Lucy algorithm 25 running under the open source Deconvolution Lab 2 v 2.0.0, with a theoretical point spread function 26 . The Trainable Weka Segmentation Plugin v. 3.1.0, a classification tool based on machine learning in FIJI 27 was applied on each deconvolved 3D-ROI so as to create a template that would automatically find the cell boundaries by providing trainable examples of membranes and cytosol (set as background).…”