BatchDeconvolution: a Fiji plugin for increasing deconvolution workflow

Introduction

Fluorescence microscopy is one of the most commonly used techniques in life sciences. The use of fluorescent probes allowed to tag specific proteins and structures within live or fixed cells and image them with high contrast and resolution [1]. Using a relatively low-cost fluorescence microscope, it is possible to achieve the lateral resolution as low as 200 nm [2]. Unfortunately, it is much harder to reach high resolution along the Z optical axis, therefore three-dimensional (3D) imaging is much more challenging [2]. There is a number of microscopy techniques allowing 3D imaging, such as confocal microscopy, 3D structured illumination microscopy, 3D stochastic optical reconstruction microscopy/photoactivated localization microscopy or light sheet microscopy, but most of them require additional, usually expensive, modules, which have to be mounted on a microscope [3].

Deconvolution microscopy is one of a few techniques that do not require any additional hardware. Based on a computational image reconstruction, it can improve both lateral and axial resolutions, allowing 3D imaging without any additional equipment cost [4]. Nevertheless, one of the main drawbacks of this technique is the requirement of vast amount of calculations, what significantly extends image preparation time [5]. Secondly, in comparison to other techniques, the intensity-based quantitative analysis on deconvolved images is much more challenging due to nonlinear operations required for image reconstruction [6].

An image of a single bright point is visible as a characteristic diffraction-based pattern called point spread function (PSF) (Figure 1A) which is specific for every optical set-up [4]. It is shaped due to physical limitation of light, physical limitations of imaging environment, and also defects and imperfections present in the imaging system. An image of a physical object (e.g. a cytoskeleton or an organelle) is a superposition of many single bright points convolved with the PSF [4]:

1. image=object∗PSF.

Figure 1:

Panel A shows exemplary cross-section images of a point spread function (PSF) generated with PSF Generator using Gibson-Lanni algorithm [22]. The green lines show the orientations of the cross-sections. Image saturation level was set to 0.01 (high saturation). PSF generation parameters: NA = 1.4, n_i = 1.518, n_s=1.33, λ = 561 nm, t_i = 170 µm, z_p = 2,000 nm. Based on a study by Kirshner et al. [8]. Panel B shows an example of a deconvolved image of immunofluorescence staining of proteasomes (PSMA2 marker) in MDA-MB-231 cells (full description in a study by Baster et al. [20]). The image was collected as a z-stack of 21 slices, one of the representative intermediate slices is presented. The brightness and contrast parameter was chosen to cover 50–99% of the pixel brightness histogram, separately in the predeconvolution and postdeconvolution images. Mpl-inferno LUT was used. Panel C shows the microscopy image acquisition-analysis pipeline. After acquisition, depending on a type of microscopy and analysis required, image may be enhanced (e.g. reconstructed or deconvolved) and then subjected to image analysis, or analysed without any additional intermediate steps. The final step is the data extraction, statistical analysis (if applicable), and preparation of a figure for presentation. Each of the steps can be automated to some extent.

The goal of deconvolution algorithms is to reverse this process. There are two ways of obtaining a PSF. First is to measure the actual PSF on a microscope using, for example, nanometre-sized fluorescent beads [7]. This solution accounts for many of the deviations in the experimental set-up (such as flaws in the optical pathway), but the resolution of the PSF is limited by a signal-to-noise ratio, and it requires a demanding experimental procedure [7], [8]. The second approach is to calculate a theoretical PSF using one of the models approximating the optical set-up [8]. It is not restricted by the resolution limitations and it is noise free but usually cannot imitate defects of the optical set-up, as well as the experimental one [5]. Moreover, depending on the complexity of the model and its accuracy, the calculations can be highly time consuming. Whether it is better to use an experimental or a theoretical PSF it needs to be evaluated on a case-by-case basis [5].

There are several deconvolution algorithms that depend on different approaches and mathematical models and have different strengths and limitations [4], [9]. Most of them (if not all) carry out calculations in Fourier space to simplify operations [10]. Deconvolution can be applied to regular wide-field microscopy and also to standard 3D techniques, such as confocal microscopy, in order to further increase contrast and resolution (Figure 1B) [11], [12]. Beside reducing out of focus light, deconvolution can also be used for deblurring single-slice images.

There are several commercially available deconvolution software such as Huygens (Scientific Volume Imaging) or AutoQuant X3 (Media Cybernetics) allowing batch processing of many images. Their main drawback is their high cost. There are several free, usually open-source, alternatives, such as DeconvolutionLab and DeconvolutionLab2 (Biomedical Imaging Group, EPFL) [9], Iterative Deconvolve 3D (OptiNav) [13], or Parallel Iterative Deconvolution (Piotr Wendykier) [14]. They are in general less user-friendly than commercial ones and they have limited batch processing capabilities, especially in cases requiring more than one PSF such as multichannel images.

The microscopy image acquisition-analysis pipeline can be separated into several stages, that, to some extent, might be automated (Figure 1C). One of the facts advocating for automatization, is that it increases data processing reproducibility. Furthermore, it also serves as an additional pair (or rather many pairs) of hands allowing one to address one’s attention to other matters, after starting calculations.

In this work, we present BatchDeconvolution a bridging Fiji plugin, allowing a batch processing of multiple optical microscopy images. The software employs currently available programs and plugins for PSF generation and deconvolution, binding them together, providing a user-friendly deconvolution platform of multiposition, time-lapse, multichannel, z-stack raw (“straight from a microscope”) image files.

Material and methods

PSF Generator [8] v. 18.12.2017 was downloaded from http://bigwww.epfl.ch/algorithms/psfgenerator/, and its source code was obtained from https://github.com/Biomedical-Imaging-Group/PSFGenerator. DeconvolutionLab2 [9], version 2.1.2 was downloaded from http://bigwww.epfl.ch/deconvolution/deconvolutionlab2/, and its source code was obtained from https://github.com/Biomedical-Imaging-Group/DeconvolutionLab2. FFTW2 dynamic libraries for DeconvolutionLab2 were downloaded from http://bigwww.epfl.ch/deconvolution/deconvolutionlab2/.

The software was developed using Java 1.8 (Oracle) in NetBeans IDE 8.2 environment (Oracle). It was tested using Fiji [15] and ImageJ [16].

Results and discussion

During our research in the field of cell migration and mechanotransduction, we have found that using elastic polyacrylamide gels [17] to mimic tissue stiffness causes blurring and distortion of microscopy images. It is a result of a more complex optical set-up than in a case of cells plated directly on a glass or a plastic substrate. This phenomenon impedes the analysis of obtained images. By applying image deconvolution, we were able to improve quality of these images. Unfortunately, at that point none of the freely available software allowed us a batch files processing to the satisfactory extent. Furthermore, because we were often collecting confocal images of different image and voxel sizes, we needed to calculate a PSF matching every of our images. To address this problem, we checked several ImageJ/Fiji plugins allowing to calculate a theoretical PSF and to deconvolve images. We determined that the most suitable programs to use in a batch processing would be PSF Generator [8] and DeconvolutionLab (and later DeconvolutionLab2, that was applied in the final version of our software) [9] from Biomedical Imaging Group, EPFL. First, both are accessible from a command line (Fiji or system), what is essential for scripting. Secondly, they both offer a vast range of algorithms and a user-friendly environment. Although, DeconvolutionLab and DeconvolutionLab2 provide an option of batch image processing, their application is limited. Thus, we decided to develop a separate software that would meet our requirements (see Table 1 for comparison of batch processing algorithms properties between programs).

We implemented both programs into our new bridging Fiji plugin we named BatchDeconvolution. The graphical user interface was created to mirror most of the options from the base programs (Supplementary Figure 1). We implemented also BioFormats repository [18] to enable read-out of most of the popular image formats including their metadata. The access to metadata allowed us to process images of a different voxel and image sizes and number of time points. Therefore, the only common parameter between image files has to be their channel structure which is defined ununiformly between file formats. Because calculations create a high amount of intermediate data (from 2 to 9 times of the size of the original data), we made our plugin to process files in a sequential manner with several check points allowing to delete expendable files (Figure 2, Supplementary Data: Pseudocode). We also implemented option for providing an external PSF (e.g. experimental).

Figure 2:

A simplified flowchart of the BatchDeconvolution’s algorithm.

Conclusions

In our work, we bridged PSF Generator and DeconvolutionLab2 programs with a Fiji plugin, and we named it BatchDeconvolution. We found our solution very useful for processing and analysing large amount of microscopy images. BatchDeconvolution provides a user-friendly, intuitive interface, allowing to process a large number of images without a requirement of any additional Fiji scripting. Applying BioFormats repository allows the software to access metadata of many file formats, that simplifies preparation of program input files and parameters.

Footnotes