Advanced microscopy techniques yield outstanding images (3D, time-lapse, multichannel), allowing one to address fundamental questions in developmental biology, molecular biology and neuroscience. Most of these techniques deploy computational methods that numerically reconstruct high-resolution or super-resolution images from the degraded measurements. A faithful reconstruction of a 3D image requires knowledge of the image acquisition model which mainly consists of the 3D point-spread function (PSF). In this presentation, I shall review two such techniques that highly rely on the PSF: 1) 3D deconvolution microscopy that helps to remove the out-of-focus and to improve the contrast of 3D images, and 2) 3D single-molecule localization microscopy that allows one to achieve super-resolution images (∼25 nm in the lateral plane, ∼75 nm in the axial direction). This presentation is based on our experience of organizing a grand challenge to benchmark a wide range of softwares on the same reference datasets.

Recent advances in microscope technology combined with powerful computers now provide outstanding images (3D, time-lapse, multichannel, fluorescence), allowing one to address fundamental questions in developmental biology, molecular biology and neuroscience. The analysis of this unprecedented flow of imaging data requires the development of specific software tools to numerically reconstruct images and to automatically perform segmentation, quantification and tracking of structures of interest. This has led to the emergence of a new field of research, #bioimage informatics# which aims to develop computational procedures to process, analyze, and visualize light microscopy images. Here, we report our experience in the development of open-source software tools. These tools are written as Java plugins for the popular open-source software suites: ImageJ, Fiji or Icy. We address the following topics with are common problems in computational bioimaging: the 3D microscopy deconvolution, the directional image analysis for detecting filament-like structure, the deformable model (snake) for segmentation, and the tracking of bright spots in noisy images.

The 3D SMLM reconstruction is a challenging computational task in term of performances, parametrization and runtime. The goal of this workshop is to present and to experiment some of the existing software solutions. We selected some software based on their usability and their accessibility (open-source) on ImageJ or Matlab. We will experiment software in four different modalities: 2D, astigmatism, double-helix, and biplane, both on simulated datasets and real datasets.

Epifluorescence microscopy results in blurry images with a very coarse optical sectioning; this limits its usefulness for cellular imaging, where resolving subcellular structures requires resolution close to or even beyond Abbe's diffraction limit. Techniques such as confocal microscopy (CLSM) and selective plane illumination microscopy (SPIM) have been proposed to reduce the out-of-focus light and to improve the resolution. Several other modalities, such as single-molecule localization microscopy (SMLM) and structured illumination microscopy (SIM) make the use of multiple acquisitions, trading time for resolution. These last techniques have been recently extended to 3D imaging.

Recent advances in microscope technology combined with new digital tools now provide outstanding images (3D, time-lapse, multichannel, fluorescence), allowing us to address fundamental questions in developmental biology, molecular biology and neuroscience. The analysis of this unprecedented flow of imaging data requires the development of sophisticated software packages to numerically reconstruct images and to automatically perform segmentation, quantification and tracking of structures of interest. This has led to the emergence of a new field of research, #bioimage informatics,# which aims to develop computational procedures to process, analyze, and visualize images coming from various light microscopy techniques. Here, we report our experience in the development of open-source software tools. These tools are written as Java plugins for the popular software suites: ImageJ, Fiji or Icy. In particular, we are focusing on the reconstruction of images from incomplete data measurements. This is often a challenging image-processing task in terms of algorithmic tuning and computational runtime. In this context, we show the importance of carefully identifying the image formation model in properly designing algorithms. We describe bioimaging applications such as restoration of details with deconvolution methods, recovery of shape from phase images, segmentation of cellular compartments from the photobleaching decay, reconstruction of nanoscale images by applying super-resolution localization microscopy, and generation of theoretical point-spread functions. Often overlooked in software development, the validation and usability are finally what counts for the end-users. In this respect, we report our effort to propose reference datasets and quantitative benchmarks of software through the organization of Grand Challenges.

In this presentation, we first give an overview of the underlying concepts of the image processing including: basic elements of digital signal-processing theory, pixelwise operations, digital filtering and an introduction to image analysis. Then, we show how the image-processing algorithms can be efficiently implemented as Java plugins for the open-source platform ImageJ/Fiji for the benefit of the research community. We cover the image preparation (correction for drift by intensity-based registration, correction for photobleaching, correction for non-uniform illumination), the image restoration (extended depth-of-field procedure, denoising, 3D microscopy deconvolution), the image analysis (feature identification, segmentation, active contour, directional image analysis) and tracking of biological fluorescent particles (spot tracking). The presentation includes intuitions and concepts of algorithms, their implementations running as ImageJ/Fiji plugins and applications to biological microscopic images. This is joint work with members of the Biomedical Imaging Group at the EPFL who have contributed to this effort over the years.

ImageJ and its distribution Fiji are widely-used public-domain software for live cell imaging applications. The open architecture of ImageJ/Fiji provides extensibility via recordable macros and Java plugins; this simulates the creativity of the developers and facilitates the dissemination of algorithms to the biological community. ImageJ, the defacto standard in bioimage software, federates people from different fields: biology, neuroscience, imaging science, microscopy, computer science in a community promoted by a new group: the Open Bio Image Alliance. In this context, we present a collection of image-processing algorithms useful for microscopy and biological applications. The presentation includes intuitions and concepts of algorithms, their implementations running as ImageJ plugins and an application to biological microscopic images. We cover the image preparation (correction for drift by registration, correction for photobleaching, correction for non-uniform illumination), the image restoration (extended-of-field procedure, denoising of fluorescence images, 3D microscopy deconvolution by PSF modelling), the image analysis (feature identification, segmentation by parametric active contour, directional image analysis) and tracking of biological particles (spot tracking). Most of the plugins have been developed by researchers of the Biomedical Imaging Group at the EPFL during the past ten years. We have made them freely available and accessible to end-users.

In the last decade, images have become indispensable to understand the structure of the cell organisms and revealing their dynamic interactions. Imaging often plays a key role in discoveries in biology. Structures or particles of interest are tagged with fluorescent probes and imaged with a new generation of 3D high-resolution microscopes which produce a large amount of data for quantitative analysis. Automatic processing of these microscopic images remains a challenging task for the image-processing community, one has to handle multidimensional data often corrupted by a defocussing effect, non-uniform lightning, or important noisy and to deal with living particles that rapidly move, grow, interact, or divide. In this tutorial, we describe several algorithms the restoration and analysis of microscopic images of biological organisms. These algorithms are implemented as Java plugins for ImageJ which is the most popular public-domain image-processing software package in the field of bioimaging. We cover the image preparation (correction for drift by registration, correction for photobleaching, correction for non-uniform illumination), the image restoration (extended-of-field procedure, denoising, deconvolution by PSF modelling) and image analysis (feature identification such as spots or filaments, segmentation by parametric active contour, directional analysis, and tracking). These algorithms have been developed by the Biomedical Imaging Group of the EPFL. While they are based on solid signal-processing fundaments, we have made them freely available and accessible to end-users. The presentation includes concepts of algorithms, applications to life cell imaging and live demonstrations.