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 micro-scopy 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.
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Methods, Elsevier, 2017, 115, pp.28-41. 〈10.1016/j.ymeth.2016.12.015〉
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Contributeur : Ferréol Soulez <>
Soumis le : lundi 13 novembre 2017 - 15:14:39
Dernière modification le : mercredi 19 septembre 2018 - 01:34:26
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DeconvolutionLab2: An open-sou...
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Daniel Sage, Lauréne Donati, Ferréol Soulez, Denis Fortun, Guillaume Schmit, et al.. DeconvolutionLab2: An open-source software for deconvolution microscopy. Methods, Elsevier, 2017, 115, pp.28-41. 〈10.1016/j.ymeth.2016.12.015〉. 〈insu-01632774〉



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