Format

Send to

Choose Destination
Methods. 2017 Feb 15;115:28-41. doi: 10.1016/j.ymeth.2016.12.015. Epub 2017 Jan 3.

DeconvolutionLab2: An open-source software for deconvolution microscopy.

Author information

1
Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Electronic address: daniel.sage@epfl.ch.
2
Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Electronic address: laurene.donati@epfl.ch.
3
Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Electronic address: ferreol.soulez@epfl.ch.
4
Center for Biomedical Imaging-Signal Processing Core (CIBM-SP), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Electronic address: denis.fortun@epfl.ch.
5
Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
6
BioImaging and Optics Platform, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
7
Center for Biomedical Imaging-Signal Processing Core (CIBM-SP), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

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.

KEYWORDS:

Deconvolution microscopy; Open-source software; Reference datasets; Standard algorithms; Textbook approach

PMID:
28057586
DOI:
10.1016/j.ymeth.2016.12.015
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center