Community and Industry workshops info
Several sessions which address key topics from image processing to advanced imaging, prepared and presented by your peers
Series of presentations on technical advances and applications directly from manufacturers, will occur in a dedicated room
Series of presentations on technical advances and applications directly from manufacturers, will occur directly at the manufacturer's booth
» Arrate Munoz Barrutia
» Hugo Botelho
» Joaquim Soriano Felipe
» Bridging Deep Learning to ImageJ - Arrate Munoz Barrutia
Machine learning (ML), and in particular, Deep Neural Networks (DNN), have become an inflexion point in many areas of scientific research. In the case of biomedical image analysis, these new techniques provide significant improvements in most of the tasks such as denoising, super-resolution, segmentation, detection, tracking, response prediction or computer aided diagnosis.
Nonetheless, the use of DNN models requires previous programming knowledge and expertise, which makes them unapproachable to the general public. Therefore, the spread of this technology to the scientific community is strongly limited. In this workshop, we present a friendly interface for the use of these models that have been developed in collaboration between EPFL and UC3M. This ready-to-use ImageJ/FIJI plugin has the potential to make available many of the powerful algorithms for image processing that are continuously being developed and published, enhancing research.
» Analysis of time lapse microscopy data with CellProfiler et al - Hugo Botelho
Automated microscopy is an invaluable tool in the research of complex cellular/organismal processes because it enables reproducible highly multi-dimensional imaging experiments (e.g. multi-position xyλt, where each position may represent a different experimental treatment across a multi-well plate). Scenarios like this are common in live cell assays or high content screening campaigns. Unfortunately, batch image analysis and proper result annotation (experimental metadata) may not be trivial for many researchers. Herein, I propose to demonstrate using CellProfiler in analyzing a small time lapse microscopy dataset with the goal of comparing the phenotypic response of intestinal organoids to gene editing and pharmacological intervention https://github.com/hmbotelho/cellprofiler-practical2-NeuBIAS). The exercise will focus on how the following basic image analysis tasks are implemented in CellProfiler: background subtraction, object segmentation, feature extraction and results output). A pre-configured analysis pipeline will be provided for the users which wish to follow the demonstration in their own laptops. In the last few minutes I will show how the same
dataset could be analyzed in Knime or Fiji. Participants will not be able to follow along on their own computers (otherwise, the demonstration would be too lengthy) but pre-configured workflows/scripts will be made available as well.
Depending on available time and participant interest, advanced operations (e.g. object-level quality control, tracking and downstream data analysis) may also be demonstrated.
» Developing imageJ macro routines on FIJI - Joaquim Soriano Felipe
- Image analysis workflows that reduce complex analysis to simple steps
- Software applications to overcome proprietary software limitations
» Pedro Matos Pereira (@P_Matos_Pereira)
» Siân Culley (@SuperResoluSian)
Despite the widespread use of Super-Resolution techniques, most methods remain technically challenging and involve sophisticated hardware and/or software tools. In 2014, Expansion Microscopy (ExM) was introduced as an alternative to overcome this challenge. The concept is extremely simple: instead of super-resolving your target of interest you physically expand your sample in such a way that the structure is now above the diffraction limit (the expansion is 4 to 5 fold the original size) and image it on a conventional microscope. The original method served to deliver super-resolution information to researchers without access to a super-resolution system. However, ExM versatility was limited by technical details like the need for special labelling probes. Several improvements were made since then to enhance its applicability, such as introducing changes that made ExM compatible with conventional probes, like commercially available labelled antibodies and endogenous fluorescent proteins. Additionally, ExM can also be combined with classical super-resolution microscopy techniques effectively allowing to combine the resolution increase from both approaches. ExM has been applied to a variety of samples, from eukaryotic cells and tissues to pathogens such as bacteria or virus, making it an excellent alternative to classical super-resolution approaches.
» Sébastien Tosi
» Ignacio Arganda Carreras
Deep learning, the latest extension of machine learning, has pushed the accuracy of algorithms to unseen limits, especially for perceptual problems such as the ones tackled by computer vision and image analysis. This workshop will cover the foundations of the field, the communities organized around it, and some important tools and resources to get started with these techniques. Some successful applications of deep learning, especially in the field of bioimage analysis, will be presented and some hands-on will cover how to setup a working deep learning environment. No prior programming knowledge is required to follow the workshop.
» Julien Colombelli
» Gabriel Martins
Mesoscopy is a new generic term that comprises the techniques allowing 3D fluorescence imaging of very large samples in toto (mm to centimeters). Mesoscopy has seen a fast pace of development recently due to improvements in light-sheet microscopy, optical tomography, and tissue clearing techniques. Because of the small offer of commercial systems to do mesoscopy, several labs have, in the past 5-6 years, begun developing DIY and even open source solutions for implementing these techniques, for example the openSPIM, OpenSpin microscopy, OPenT, MesoSPIM, LegoLISH or OptiSPIM.
In this small workshop we will demonstrate the principles of light sheet and optical tomography, some of the subsystems necessary to assemble a working mesoscope and a brief demo of how 3D mesoscopic datasets are acquired.
» Erin Tranfield
» Mafalda Silva
» Ana Laura Sousa
Correlative Light and Electron Microscopy: Introduction to experimental
procedures, as well as tricks and tips for successful experiment planning and execution.
1. Intro to the different kinds of CLEM and some of the challenges of CLEM
2. CLEM applications: how to answer biological questions
3. Discussion about how to start a project and when CLEM is an appropriate
4. Demonstration of CLEM tools
» Siân Culley (@SuperResoluSian)
» Pedro Matos Pereira (@P_Matos_Pereira)
In this session, we will look at Fiji workflows for single molecule localization microscopy (SMLM) data. The main points that we will cover are:
1. Analysis of sparse blinking datasets: the ‘classic’ SMLM dataset
• Use of QuickPALM and ThunderSTORM to localize individual molecules
• Interpretation of particle tables
• Drift correction
• Rendering and visualisation of images
2. 3D SMLM data sets
• Different types of 3D calibration data
• Localization and visualisation in ThunderSTORM
3. Dense blinking datasets: the ‘tricky’ SMLM dataset
• Use of HAWK for pre-processing dense data
• Options in ThunderSTORM for fitting dense data
• Using SRRF for live-cell data
4. Multicolour datasets
• Chromatic aberration correction using NanoJ
5. Assessing data quality
• Using SQUIRREL to measure the resolution of images and to assess the quality of images.
Homework: To prepare for this session, participants are asked to install Fiji (https://imagej.net/Fiji/Downloads). We will be using the following plugins:
• QuickPALM (pre-packaged into Fiji)
• ThunderSTORM (installation instructions at: https://github.com/zitmen/thunderstorm)
• HAWK (https://www.coxphysics.com/ - copy the version 1.1 .jar file into the plugins folder in your Fiji install)
• NanoJ-Core, NanoJ-SRRF, NanoJ-SQUIRREL (In Fiji, go to Help>Update…>Manage Update Sites and check the boxes for NanoJ-Core, NanoJ-SQUIRREL and NanoJ-SRRF. Close this window, then press ‘Apply changes’
We will also be using various test data sets (which we will provide or the participants can bring their own)
» María Victoria Gomez
3D IMAGING: OPTICAL TISSUE CLEARING
3D imaging tools are emerging in the field of optics, bioengineering and biomedicine. Opacity precludes light penetration into the tissues, making it difficult to obtain accurate 3D pictures of an organ without slicing it. Optical tissue clearing techniques enable light penetration into tissues and solve the scattering issue, allowing visualization of the whole tissue without the need to section it. Through tissue clearing, 3D imaging of whole organs is bringing light to basic and clinical research, including diagnostics.
Different methods and protocols for optical tissue clearing have been developed. In this workshop we will learn the advantages and disadvantages of each of the following clearing methods:
» General optics concepts: scattering and transparency.
» Protocols and methods: advantages and disadvantages
» Immunostaining of whole organs
» BABB method
» Whole organ clearing with the CUBIC protocol:
- Advantages of the CUBIC method.
- Latest versions and optimizations of the CUBIC method» Clearing for confocal and SPIM microscopy image acquisition