The Fiji platform, a cornerstone in scientific imaging, has undergone several major developments to meet evolving community needs, focusing on cross-language integration and modernization to enhance its utility and accessibility.
Recent key initiatives include: upgrading Fiji to OpenJDK 21 with the new Jaunch launcher, which supports both JVM and Python runtimes; developing SciJava Ops for...
This workshop will give an introduction into the ImageJ-based visual programming language JIPipe. Attendees will be guided step-by-step through the process of building batch processing workflows of ImageJ-functions and JIPipe-exclusive features. All demonstrations will be based on already published projects.
The clEsperanto project equips users and developers with GPU-accelerated image-processing components. It ships a library with which you will be able to build fast bioImage analysis scripts, by leveraging modern GPUs.
Positioned as a classical Image Processing library, it is built on a framework that makes it available in many language (Python, Java, C++), in many software platforms (Fiji,...
The workshop will involve live coding, showing how one can use a Jupyter notebook for exploratory data analysis in napari and then make simple widgets based on that. The target is someone who has some familiarity with the napari application and bioimage analysis in Python and wants to take the next steps to customize/extend the napari GUI. For this, magicgui will be introduced as a way to...
Deriving scientifically sound conclusions from microscopy experiments typically requires batch analysis of large image data sets. Once the analysis has been conducted it is critical to visually inspect the results to identify errors and to make scientific discoveries. Leveraging the ImgLib2/BigDataViewer ecosystem we developed a platform in which large image data sets can be conveniently...
In life sciences, tracking objects within movies is crucial for quantifying the behavior of particles, organelles, bacteria, cells, and entire organisms. However, tracking multiple objects across numerous movies and analyzing the objects’ movements can be challenging. This workshop aims to demonstrate the effective utilization of TrackMate for object tracking across multiple movies through...
Explore Omega, an advanced conversational agent designed to enhance bioimage analysis through large language models. Participants will learn how to converse with Omega to perform basic image processing and analysis tasks, from basic filtering to more advanced tasks such as segmenting cell nuclei, creating custom image processing widgets, and applying denoising algorithms. Additionally, the...
QuPath is a popular open-source platform for visualizing, annotating and analyzing complex images - including whole slide and highly multiplexed datasets. QuPath is inspired and influenced by ImageJ and Fiji, but also quite different.
This workshop will show how experienced ImageJ and Fiji users can benefit from QuPath, and vice versa. It will show how to use ImageJ and QuPath together...
The question "How thick is my biological object?" is more difficult to answer than it appears on first sight. In this workshop we will use different methods to calculate width profiles of 2D and 3D objects and learn about their differences. We will use FIJI/ImageJ in the workshop.
Joint analysis of different data modalities is a promising approach in developmental biology which allows to study the connection between cell-type specific gene expression and cell phenotype. Normally, using analysis methods in correlative manner poses a lot of limitations; however, studying a stereotypic model organism gives a unique opportunity to jointly analyze data obtained from...
The translation of raw data into quantitative information, which is the ultimate goal of microscopy-based research studies, requires the implementation of standardized data pipelines to process and analyze the measured images. Image quality assessment (IQA) is an essential ingredient for the validation of each intermediate result, but it frequently relies on ground-truth images, visual...
With the Cell Painting assay we quantify cell morphology using six dyes to stain eight cellular components: Nucleus, mitochondria, endoplasmic reticulum, nucleoli, cytoplasmic RNA, actin, golgi aparatus, and plasma membrane. After high-throughput fluorescence microscopy, image analysis algorithms then extract thousands of morphological features from each single cell’s image. By comparing of...
We propose a new method of molecular counting, or inferring the number of fluorescent emitters when only their combined intensity contributions can be observed. This problem occurs regularly in quantitative microscopy of biological complexes at the nano-scale, below the resolution limit of super-resolution microscopy, where individual objects can no longer be visually separated. Our proposed...
In this work, we present a comprehensive Python tool designed for automated large-scale cytotoxicity analysis, focusing on immune-target cell interactions. With the capacity to handle microscopic imaging with a 24-hours imaging duration of up to 100,000 cells in interaction per frame, pyCyto offers a robust and scalable solution for high-throughput image analysis. Its architecture comprises...
Analyzing large amounts of microscopy images in a FAIR manner is an ongoing challenge, turbocharged by the large diversity of image file formats and processing approaches. Recent community work on an OME next-generation file format offers the chance to create more shareable bioimage analysis workflows. Building up on this and to address issues related to the scalability & accessibility of...
While recent advancements in computer vision have greatly benefited the analysis of natural images, significant progress has also been made in volume electron microscopy (vEM). However, challenges persist in creating comprehensive frameworks that seamlessly integrate various machine learning (ML) algorithms for the automatic segmentation, detection, and classification of vEM across varied...
Biological systems undergo dynamic developmental processes involving shape growth and deformation. Understanding these shape changes is key to exploring developmental mechanisms and factors influencing morphological change. One such phenomenon is the formation of the anterior-posterior (A-P) body axis of an embryo through symmetry breaking, elongation, and polarized Brachyury gene expression....
Dense, stroma-rich tumors with high extracellular matrix (ECM) content are highly resistant to chemotherapy, the standard treatment. Determining the spatial distribution of cell markers is crucial for characterizing the mechanisms of potential targets. However, an end-to-end computational pipeline has been lacking. Therefore, we developed a robust image analysis pipeline for quantifying the...
Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixels. Current self- and unsupervised denoisers can address signal-dependent noise, but none can reliably remove noise that is also row- or column-correlated. Here, we present the first fully unsupervised deep...
As the field of biological imaging matures from pure phenotypic observation to machine-assisted quantitative analysis, the importance of multidisciplinary collaboration has never been higher. From software engineers to network architects to deep learning experts to optics/imaging specialists, the list of professionals required to generate, store, and analyze imaging data sets of exponentially...
High-resolution atomic force microscopy (AFM) provides unparalleled visualisation of molecular structures and interactions in liquid, achieving sub-molecular resolution without the need for labelling or averaging. This capability enables detailed imaging of dynamic and flexible molecules like DNA and proteins, revealing their own conformational changes as well as interactions with one another....
We propose a new method of molecular counting, or inferring the number of fluorescent emitters when only their combined intensity contributions can be observed. This problem occurs regularly in quantitative microscopy of biological complexes at the nano-scale, below the resolution limit of super-resolution microscopy, where individual objects can no longer be visually separated. Our proposed...
Bioimage analysis research workflows often require the use of various software tools and demand significant computational power and high interactivity. These workflows can result in inconsistent results due to dependencies on particular software and operating systems, an issue that becomes especially evident as computationally intensive methods like deep learning become more common.
To tackle...
Understanding the underlying mechanisms of Autism Spectrum Disorder (ASD) requires detailed analysis of behaviour in model organisms. Here, we present a complete image analysis pipeline designed to analyze ASD-like behaviours in Drosophila, providing insights into the associated mechanisms.
Our pipeline begins with the acquisition of high-resolution, large-format videos capturing...
Immunofluorescence is a powerful technique for the detection of multiple specific markers in cells; however, the fixation process prevents the study of the cells’ motility. We therefore propose a tool that helps find back tracked cells after immunofluorescence (IF). A slice of tissue is imaged during 48h at different positions with a 20x objective, creating movies on which cells are manually...
Dense, stroma-rich tumors with high extracellular matrix (ECM) content are highly resistant to chemotherapy, the standard treatment. Determining the spatial distribution of cell markers is crucial for characterizing the mechanisms of potential targets. However, an end-to-end computational pipeline has been lacking. Therefore, we developed a robust image analysis pipeline for quantifying the...
Joint analysis of different data modalities is a promising approach in developmental biology which allows to study the connection between cell-type specific gene expression and cell phenotype. Normally, using analysis methods in correlative manner poses a lot of limitations; however, studying a stereotypic model organism gives a unique opportunity to jointly analyze data obtained from...
Plasma membrane processes like clathrin-mediated endocytosis and constitutive exocytosis are often studied using diffraction-limited imaging methods. Structured Illumination Microscopy (SIM) with Total Internal Reflection Fluorescence (TIRF) offers a super-resolution technique for these studies in living cells. However, no automated, publicly available tool has existed for processing TIRF-SIM...
Stem cells are unique in their self-renewal and differentiation capacity into many cell types. They are an attractive platform for modelling human tissue in vitro, but producing homogenous cultures is challenging due to limited control of the microenvironment. Automating differentiation protocols can reduce human error and increase efficiency and yield.
Here we present an open-source...
Sub-cellular structures that are visualized as spots with fluorescence microscopy are ubiquitous in microscopy data. However, automated and accurate detection of such spots is often a challenging task. Additionally, many microscopy datasets contain multiple channels, where in addition to the spots and the cells also a second structure is visualized, such as the nucleus in single-molecule FISH...
The past 10 years have seen an explosion of algorithms, especially deep learning algorithms, which can make bioimage analysis for specific problems vastly more straightforward. Unfortunately, the median computational comfort for biologists is not always high enough to enable them to install deep learning tools successfully, and the vast majority are not comfortable working at the terminal or...
The landscape of computational bioimaging is rapidly evolving, featuring a vast array of tools, workflows, and documentation. This growth underscores the need for more accessible analytical tools to deliver comprehensive capabilities to the scientific community. The BioImage.IO Chatbot, developed in response to this need, leverages Large Language Models (LLMs) and Retrieval Augmented...
BrainGlobe is a community-driven initiative built around a set of interoperable, user-friendly, open-source software tools for neuroanatomy. It provides atlases for human, mouse, rat and zebrafish brains through a consistent interface. Each atlas includes a “standard” reference image for sample registration and corresponding brain region segmentations. This allows data from several samples to...
A longstanding quest in biology is to understand how the fertilized egg gives rise to multiple cell types organized in tissues and organs of specialized shape, size and function to suit the lifestyle of multicellular organisms. The integration of molecular, microscopy and image analysis techniques increasingly enables scientists to quantify developmental processes in live developing embryos...
Recent advances in super-resolution fluorescence microscopy have enabled the study of nanoscale sub-cellular structures. However, the two main techniques used—Single Molecule Localization Microscopy (SMLM) and STimulated Emission Depletion (STED)—are fundamentally different and difficult to reconcile. We recently published a protocol enabling to perform both techniques on the same neuron...
We will learn how to use conv-paint, a fast and interactive pixel classification tool for multi dimensional images. A graphical user interface is integrated into the image viewer napari, but we will also learn how to script the software from the python ecosystem. As a napari-plugin, conv-paint can easily be integrated with other plugins into complex image processing pipelines, even by users...
Fluorescence microscopy is an indispensable tool to visualize spatio-temporal biological mechanisms at sub-micron resolution across all areas of life sciences. However, microscopists still face a difficult trade-off between imaging resolution, throughput, light sensitivity, and scale. In practice, balancing these factors often comes down to limiting the scope of investigation: for example,...
Microscopic imaging enables us to investigate cells and how they change, but since subtle changes are hard to see by eye, we need tools such as deep learning to help us see.
Here, we are combining label-free microscopy with deep learning to predict stem cell differentiation outcomes. This is highly relevant, as the differentiation process is labor intensive, costly and subject to high...
Immunological synapses, important in cancer immunotherapy, are transient structures formed at the interface between lymphocytes and antigen-presenting cells. To assess the efficacy of immune therapy responses, we present a bioimage analysis workflow for quantifying cell interactions and identifying immune synapses in cancer patient samples using Imaging Flow Cytometry (IFC).
IFC acquires...
Image restoration methods often suffer from the "Regression to the Mean" (R2M) effect, leading to blurry results due to their inability to restore high-frequency details. This is problematic in microscopy, where the loss of such fine details can deter subsequent analysis and downstream processing.
In this work, we propose to tackle this challenge through a data-driven approach, by...
Advances in imaging and computation have enabled the detailed recording and quantification of spatial biological processes. However, there remains a gap in the statistical analysis and modeling of spatial information. A key challenge is to discover co-observed processes that statistically explain spatial localization patterns. We address this problem in the framework of spatial point...
Cross-correlation is a versatile mathematical technique for analyzing image data. It provides insights into spatial distributions, temporal dynamics, and geometric colocalization by quantifying relationships between image components. With this project, we explore a specific application of cross-correlation, namely the autocorrelation, to boost image resolution in post-processing. We...
FBIAS is a network of image analysts that provides remote support to users lacking access to such expertises services, usually beginning with a 1-hour open-desk session followed by project-based assistance. A feasibility study to compare different multiple tracking methods applied to videos of rainbow trouts was conducted by FBIAS, inside Institut Curie in collaboration with IERP, an INRAE...
With the Cell Painting assay we quantify cell morphology using six dyes to stain eight cellular components: Nucleus, mitochondria, endoplasmic reticulum, nucleoli, cytoplasmic RNA, actin, golgi aparatus, and plasma membrane. After high-throughput fluorescence microscopy, image analysis algorithms then extract thousands of morphological features from each single cell’s image. By comparing of...
In our increasingly data-centric world, converting images into knowledge is a challenge across various scientific fields. Acknowledging the immense possibilities of this drive to visualise and understand data, the FAIR Image Analysis Workflow working group is developing FAIR image analysis workflows within the Galaxy platform. Although the Galaxy platform is well-established in scientific...
The Fiji platform, a cornerstone in scientific imaging, has undergone several major developments to meet evolving community needs, focusing on cross-language integration and modernization to enhance its utility and accessibility.
Recent key initiatives include: upgrading Fiji to OpenJDK 21 with the new Jaunch launcher, which supports both JVM and Python runtimes; developing SciJava Ops for...
Quantitatively characterizing cellular morphology is a pivotal step in comprehending cellular structure and, by extension, cellular function. Electron microscopy (EM) and expansion microscopy (ExM) are complementary techniques that grant access to the intricate world of cellular ultrastructure. Nevertheless, the absence of automated, universally applicable frameworks for extracting...
For our high-content-screening service at TEFOR-Paris-Saclay, we developed a data-management and bioimage-analysis framework, which we share here with the bioimage-analysis-community.
The filesystem-based-image-database (fsdb) is designed to help labs and core facilities manage, analyze, and archive big data while being invisible to the user of the data within.
Using Linux’s authentication...
DNA double-strand breaks (DSBs) can seriously threaten the integrity of the affected cell. To maintain its genetic information, the cell needs to repair DSBs as faithfully as possible.
In Escherichia coli, the RecBCD complex recognises and processes DSBs to generate a single-stranded DNA coated with the RecA protein, which is used to search for a homologous repair template. However, little is...
Cell cycle regulation and cell size control in yeast is governed by complex protein interactions and can vary greatly depending on genetic disposition. A powerful tool to study cell cycle progression is high-throughput live-cell imaging where dynamic cellular processes can be observed for single cells over time. However, studies based on this technique are limited in scope due to the lack of...
Machine learning (ML) is becoming pivotal in life science research, offering powerful tools for interpreting complex biological data. In particular, explainable ML provides insights into the reasoning behind model predictions, highlighting the data features that drove the model outcome. Our work focuses on building explainable ML models for microscopy images. These models not only classify...
Recent developments investigating DNA topology have led to a wealth of knowledge regarding genome stability and cellular functions. The use of Atomic Force Microscopy (AFM) has had great success in imaging high-resolution DNA topology at the nanoscale, revealing insights into fundamental biological processes, which was not previously possible using traditional structural biology techniques....
We have established long-term light sheet imaging of human brain organoid development for the spatiotemporal profiling of developmental morphodynamics underlying human brain patterning and morphogenesis. Here, we present and analyze a novel dual-channel, multi-mosaic and multi-protein labeling strategy combined with a demultiplexing approach. To achieve this, we have developed Light-Insight, a...
The translation of raw data into quantitative information, which is the ultimate goal of microscopy-based research studies, requires the implementation of standardized data pipelines to process and analyze the measured images. Image quality assessment (IQA) is an essential ingredient for the validation of each intermediate result, but it frequently relies on ground-truth images, visual...
The development of single-molecule imaging approaches to study cellular machineries reconstituted from purified components is generating more diverse and complex datasets. These datasets can reveal the dynamic motion of protein assemblies, exchange kinetics of individual components and landscape of pathways supporting essential cellular reactions. However, few solutions exist for processing...
Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope’s optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce MicroSplit, a deep learning-based computational multiplexing technique that enhances the imaging of multiple cellular structures within a...
Brain organoids enable mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems. We have established long-term (~3 weeks) lightsheet microscopy on multi-mosaic neural organoids (MMOs) generated from sparse mixing of ulabelled iPSCs with fluorescently labeled human iPSCs (Actin-GFP, Histone-GFP, Tubulin-RFP,...
Quantitative analysis of biological phenomena is practically a requirement in contemporary research, particularly when dealing with image data. AI-assisted tools simplify complex tasks like image segmentation, even for those without computational expertise. Supervised machine learning excels in classifying data from minimal manual annotations. While several software solutions exist for...
The analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie adapts to image metadata, eliminating user input. Nellie's preprocessing pipeline enhances structural contrast on multiple...
nlScript is a novel toolbox for creating unified scripting interfaces based on natural language in applications where a large number of configuration options renders traditional Graphical User Interfaces unintuitive and intricate. nlScript's concept is based on 3Dscript, where users describe in natural English sentences how 3Dscript’s rendering engine should animate volumetric microscope data...
By integrating multi-omics data into logical graph representations, systems biology aims to model biological interactions to be studied in health and disease. In this project, a multi-layered heterogeneous network was built from data made available in public databases, relating diseases and compounds to the human interactome. Morphological profiles of compound-treated cells were also included...
The increasing frequency of heatwaves we are experiencing at present strongly affects plant fertility and crops yields. While the whole plant suffers during hot periods, pollen development is especially sensitive. Our project aims to understand how some plants cope better than others with elevated temperatures. For this, we screened plants from populations with a diverse genetic background...
Image exploration and quality control (QC) are essential first steps in any bioimage analysis task. Traditionally, researchers manually inspect randomly sampled images, examine metadata, and extract image features to investigate the data. This process ensures a deeper understanding of the image data and allows for informed algorithm development. However, it often requires multiple open-source...
In this work, we present a comprehensive Python tool designed for automated large-scale cytotoxicity analysis, focusing on immune-target cell interactions. With the capacity to handle microscopic imaging with a 24-hours imaging duration of up to 100,000 cells in interaction per frame, pyCyto offers a robust and scalable solution for high-throughput image analysis. Its architecture comprises...
We are excited to introduce the community to pymmcore-plus, a new package for controlling microscopes through the open-source software Micro-Manager within a pure Python environment. pymmcore-plus is an extension of pymmcore, the original Python 3.x bindings for the C++ Micro-Manager core and, as such, it operates independently of Java, eliminating the need for Java dependencies. A key feature...
The migration of molecules and cellular organelles is essential for cellular functions. However, analysing such dynamics is challenging due to the high spatial and temporal resolution required and the accurate analysis of the diffusional tracks. Here, we investigate the migration modes of peroxisome organelles in the cytosol of living cells. Peroxisomes predominantly migrate randomly, but...
An intriguing question in cancer biology is the complex relationship between molecular profiles, as provided by transcriptomics, and their phenotypic manifestations at the cellular and the tissue scale. Understanding this relationship will enable us to comprehend the functional impact of transcriptomic deregulations and identify potential biomarkers in the context of precision medicine.
One...
"Biological imaging often generates three-dimensional images with a very large number of elements. In particular, microtomography results in images of several Giga-Bytes. The analysis of such data requires fast and efficient image processing software solutions, especially when user interaction is necessary. In some cases, algorithmic methods have been proposed but are seldom implemented within...
Budding yeasts growing in micro-fluidic devices constitute a convenient model to study how eukaryotic cells respond and adapt to sudden environmental change. Many of the intracellular changes needed for this adaptation are spatial and transient — they are observed in the dynamic localization of proteins from one sub-cellular compartment to another. These dynamics can be captured experimentally...
Reconstructing the connectome is a complex task, due to the diversity of neuronal forms and the density of the environment.
At LOB, in collaboration with IDV, we have developed a microscope combining multicolor two-photon excitation by wavelength mixing and serial block acquisition. This imaging method (Chroms), applied to a Brainbow retrovirus-labeled mouse brain, provides several...
Methods in biomedical image processing are changing very fast due to the high attention to computer vision and machine learning. Keeping up with trends and testing interesting routes in biomedicine is therefore very challenging. With this report, I would like to showcase a project-based teaching scheme that focuses on a single biomedical topic and aims to create progress in this field. Here,...
High-resolution atomic force microscopy (AFM) provides unparalleled visualisation of molecular structures and interactions in liquid, achieving sub-molecular resolution without the need for labelling or averaging. This capability enables detailed imaging of dynamic and flexible molecules like DNA and proteins, revealing their own conformational changes as well as interactions with one another....
As a biologist with a new dataset the cost of labelling and training a new ML model is prohibitive to downstream analysis. One solution is to utilise pre-trained networks in a transfer learning approach. Community efforts such as the BioImage-Model-Zoo have increased the availability of pre-trained models. However, how should the best pre-trained model for a particular task and dataset be...
Recent new developments in microscopy image analysis are heavily biased towards blob-like structures (cells, nuclei, vesicles), while tools for the exploration of filaments or curvilinear objects (spines, biopolymers, neurites, vessels, cilia) are often underrepresented. To address this gap, we developed the BigTrace plugin for Fiji, powered by the imglib2 library and using BigVolumeViewer for...
Epithelial tissue dynamics is tightly controlled spatio-temporally in numerous developmental processes and this precision is essential for the correct formation and homeostasis of organs. Quantifying epithelia dynamics is an important step in understanding this regulation but usually involves huge movies spanning thousands of cells and several hours. Thus, efficient and user-friendly tools are...
The lack of screenable phenotypes in scalable cell models has limited progress in drug discovery and early diagnostics for complex diseases. Here we present a novel unbiased phenotypic profiling platform that combines high-throughput cell culture automation, Cell Painting, and deep learning. We built various models to extract meaningful features at single cell level, including deep learning...
Deriving scientifically sound conclusions from microscopy experiments typically requires batch analysis of large image data sets. It is critical that these analysis workflows are reproducible. In addition, it is advantageous of the workflows are scalable and can be readily deployed on high performance compute infrastructures.
Here, we will present how the established Nextflow workflow...
Modern microscopy technologies, such as light sheet microscopy, enable in toto 3D imaging of live samples with high spatial and temporal resolution. These images will be in 3D over time and may include multiple channels and views. The computational analysis of these images promises new insights into cellular, developmental, and stem cell biology. However, a single image can amount to several...
"The BioImage.IO Chatbot (https://github.com/bioimage-io/bioimageio-chatbot) is a chat assistant we created to empower the bioimaging community with state-of-the-art Large Language Models. Through the help of a series of extensions, the BioImage.IO Chatbot is able to query documentation and retrieve information from online databases and image.sc forum, as well as generating code and executing...
We will introduce the IPA Project Template, which offers a structured way to organize image processing and analysis projects. Our template is designed to support users throughout their projects, providing space for experimentation, organizing proven methods into reproducible steps, tracking processing runs, and facilitating documentation.
During the workshop participants will create their...
In this workshop, we will demonstrate Piximi, an images-to-discovery web application that allows users to perform deep learning without installation. We will demonstrate Piximi's ability to load images, run segmentation, perform classifications, and create measurements, all without the data leaving the user's computer.
QuPath is a popular, open-source platform for visualizing, annotating and analyzing complex images - including whole slide and highly multiplexed datasets. QuPath is written in Java and scriptable with Groovy, which makes it portable, powerful, and... sometimes a pain if you'd rather be working in Python (sometimes we would too).
This workshop will show how QuPath and Python can work...
The workshop will show how Segment Anything, a deep learning model for interactive instance segmentation, can applied to segmentation and annotation tasks in biomedical images. We will first give an overview of different approaches that apply this method in the biomedical domain. Then we will introduce our tool, Segment Anything for Microscopy,
which introduces specialized models for...
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, interactively viewing & 3D-aligning, as well as rendering sequence-based spatial transcriptomics data (e.g. Slide-Seq), which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji (https://github.com/PreibischLab/STIM). In contrast to the ""classical"" sequence analysis space, STIM relies on...
Our team has created TopoStats, an open-source Python toolkit for automated processing and analysis of Atomic Force Microscopy (AFM) images. We wish to provide a hands-on session to guide users through the TopoStats workflow, from raw AFM file formats through to molecule segmentation, quantification and biological interpretation. We will demonstrate how users can install TopoStats and discuss...
DaCapo is a deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. It addresses the need for scalability and 3D-aware segmentation networks, and is developed with a modular and open-source framework for training and deploying deep learning solutions at scale. DaCapo breaks through terabyte-sized...
BigWarp is an intuitive tool for non-linear manual image registration that can scale to terabyte-sized image data. In this workshop, participants will learn to perform image registration with BigWarp, apply transformations to large images, import and export transformations to and from other tools, and fine-tune the results of automatic registration algorithms. BigWarp makes heavy use of the...
In this workshop, we will introduce users to CellProfiler, a software that allows you to create reproducible image analysis workflows without knowing how to code. Users will be introduced to how to input multi-channel data into CellProfiler, how to perform image processing, object finding, and measurement steps, as well as how to create reproducible outputs.
In this workshop, we present computational tools, workflows and pipelines for the processing and analysis of multiplexed tissue imaging data. In the first part of the workshop, we discuss commonly used analysis strategies and present a set of existing pipelines for multiplexed image processing, including MCMICRO (Schapiro et al., 2022), Steinbock (Windhager et al., 2023) and PIPEX...
In some cases, meshes have advantages over the use of voxels for image analysis (lightweight representation, surface features, ...)
In this workshop, participants will learn:
- How to pass from a voxels representation to a mesh, and vice-versa.
- How to use Blender and Napari as well as some dedicated Python modules to filter, process or extract measures from meshes.
The workshop will introduce PlantSeg 2.0: a new version of the popular PlantSeg tool for segmentation of cells in tissues based on membrane staining, in 2D and in 3D. Together with the participants, we will step through the new napari-based GUI, learning to choose pre-trained networks, set the parameters, correct the results and launch headless jobs. No prior experience with PlantSeg is needed.
We - Jerome Mutterer and Jan Brocher - would like to introduce participants in this joint hands-on workshop to discover the flexible potential of ImageJ or Fiji alongside Inkscape for crafting scientific figures, slides, or posters. Our aim is not only to effectively incorporate traditional elements such as scale bars or labels but also to integrate reproducible content whenever possible. The...
This hands-on workshop will introduce you to CytoDL, a powerful deep learning framework developed by the Allen Institute for Cell Science. CytoDL is designed to streamline the analysis of biological images, including 2D and 3D data represented as images, point clouds, and tabular formats. We will cover (A) Getting single cell and nucleus instance segmentations from image datasets from the...
Multi-view and multi-tile imaging offer great potential for enhancing resolution, field of view, and penetration depth in microscopy. Here we present multiview-stitcher, a versatile and modular python package for 2/3D image reconstruction that leverages registration and fusion algorithms readily available within the ecosystem for efficient use within an extensible and standardized framework. A...
Lateral inhibition mediates the adoption of alternative cell fates to produce regular cell fate patterns, with fate symmetry breaking (SB) relying on the amplification of small stochastic differences in Notch activity via an intercellular negative feedback loop. Here, we used quantitative live imaging of endogenous Scute (Sc) to study the emergence of Sensory Organ Precursor cells (SOPs) in...
The analysis of gene expression within spatial contexts has greatly advanced our understanding of cellular interactions. Spatial transcriptomics, developed through both indexing-based sequencing and microscopy-based technologies, offers unique benefits and challenges. Indexing-based methods achieve high resolution but are costly, while microscopy-based methods, such as in situ sequencing...
This project introduces a novel microscope image acquisition plugin for QuPath designed to enhance data-driven image collections by connecting a user-friendly image analysis tool with PycroManager and MicroManager for microscope control. QuPath provides quick and easy tools to generate user-defined annotations within a whole slide image to guide image collections at higher resolutions or with...
"Digital pathology and artificial intelligence (AI) applied to histopathological images are gaining interest in immuno-oncology for streamlining diagnostic and prognostic processes. This study aimed to develop a computational pipeline to analyze H&E-stained cancer tissues and identify clinically relevant tumor microenvironment features.
Our pipeline employs machine and deep learning...
"Cell division is a fundamental process in cell biology, which comprises two main phases: mitosis (nuclear division) and cytokinesis (cytoplasmic division). Despite extensive research over several decades, our understanding of cell division remains incomplete. One approach to investigate the role of specific genes in cytokinesis consists in inhibiting the genes in a cell line and to observe...
Despite advancements in oncology, triple-negative breast cancer (TNBC) remains the most aggressive subtype, characterized by poor prognosis and limited targeted treatments, with non-specific chemotherapy as the primary option. To uncover new biological mechanisms, we focus on the mechanobiology of TNBC, examining how cells and tissues perceive and integrate mechanical signals, known as...
"The handling, analysis, and storage of image data present significant challenges in a wide range of scientific disciplines. As the volume and complexity of image data continue to grow, researchers face key challenges like scalability, analysis speed, reproducibility and collaboration with peers. Containerisation technologies like Docker offer solutions to many of these challenges by providing...
Bioimage analysis is essential for advancing our understanding of cellular processes, yet traditional methods often fall short in scalability and efficiency. To address these challenges, our research focuses on developing a comprehensive infrastructure integrating data streaming and collaborative annotation for training large foundation models. Our streaming dataloader efficiently manages...
In the deep learning era, the client-server approach for bioimage analysis offers significant advantages, enhancing both extensibility and accessibility. The image analysis server can be implemented either locally or remotely, enabling efficient resource allocation while offering a wide variety of choices for the client application. Researchers can leverage powerful GPU devices suitable for...
One frequent task performed on high-resolution 3D time-lapse microscopy images is the reconstruction of cell lineage trees. The construction of such lineage trees is computationally expensive, and traditionally involves following individual cells across all time points, annotating their positions, and linking them to create complete trajectories using a 2D interface. Despite advances in...
State-of-the-art serial block face scanning electron microscopy (SBF-SEM) is used in cellular research to capture large 2D images of sliced tissue. These 2D images collectively form a 3D digital representation of the tissue. SBF-SEM was recently used to reconstruct the first 3D ultrastructural analysis of neural, glial, and vascular elements that interconnect to form the neurovascular unit...
Biological systems undergo dynamic developmental processes involving shape growth and deformation. Understanding these shape changes is key to exploring developmental mechanisms and factors influencing morphological change. One such phenomenon is the formation of the anterior-posterior (A-P) body axis of an embryo through symmetry breaking, elongation, and polarized Brachyury gene expression....
Zarrcade is a web application designed to make it easy to browse collections of OME-Zarr images. OME-Zarr is a modern file format gaining popularity in the bioimage community. Despite its cloud compatibility, it's challenging for users to make collections of these images accessible, searchable, and browsable on the web. Zarrcade addresses this by allowing users to quickly create searchable...
DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large near-isotropic image data. In this correspondence, we introduce DaCapo’s unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss...
Recent advances in unsupervised segmentation, particularly with transformer-based models like MAESTER, have shown promise in segmenting Electron Microscopy (EM) data at the pixel level. However, despite their success, these models often struggle with capturing the full hierarchical and complex nature of EM data, where variability in texture and the intricate structure of biological components...
Digital pathology combined with AI is revolutionizing oncoimmunology by enhancing diagnostic workflows and analytical outputs. This study integrates different histopathological methods with high-throughput computational imaging to analyze the tumor microenvironment (TME).
We began by analyzing tumor tissue and structure using H&E-stained slides and computational methods. A deep learning...
High-performance computers (HPC) are essential for bioimage analysis, however the barrier to entry can be high. This project aims to simplify access to bioimage analysis tools and deep learning models on local HPC clusters, enabling frictionless access to software and large computation.
Inspired by the Bioimage ANalysis Desktop (BAND) and ZeroCostDL4Mic, we developed lightweight bash...
Determining mechanism of action (MoA) for antimicrobial compounds is key in antibiotic discovery efforts. Bacterial Cytological Profiling (BCP) is a rapid one-step assay utilising fluorescent microscopy and machine learning to discriminate between antibacterial compounds with different MoAs and help predict the MoA of novel compounds. One barrier to BCP being adopted more widely is a lack of...
While recent advancements in computer vision have greatly benefited the analysis of natural images, significant progress has also been made in volume electron microscopy (vEM). However, challenges persist in creating comprehensive frameworks that seamlessly integrate various machine learning (ML) algorithms for the automatic segmentation, detection, and classification of vEM across varied...
Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have...
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. Phasor analysis is a fit-free technique based on a FFT transformation of the intensity decay that provides a visual distribution of the molecular species, clustering pixels with similar lifetimes even when they are spatially separated in the image. Phasor analysis is...
The 3D morphology of the cell nucleus is traditionally studied through high-resolution fluorescence imaging, which can be costly, time-intensive, and have phototoxic effects on cells. These constraints have spurred the development of computational ""virtual staining"" techniques that predict the fluorescence signal from transmitted-light images, offering a non-invasive and cost-effective...
Analyzing large amounts of microscopy images in a FAIR manner is an ongoing challenge, turbocharged by the large diversity of image file formats and processing approaches. Recent community work on an OME next-generation file format offers the chance to create more shareable bioimage analysis workflows. Building up on this and to address issues related to the scalability & accessibility of...
As the field of biological imaging matures from pure phenotypic observation to machine-assisted quantitative analysis, the importance of multidisciplinary collaboration has never been higher. From software engineers to network architects to deep learning experts to optics/imaging specialists, the list of professionals required to generate, store, and analyze imaging data sets of exponentially...
Quantitative analysis of bioimaging data often depends on the accurate segmentation of cells and nuclei. This is both especially important and especially difficult for the analysis of highly multiplexed imaging data, which can contain many input channels. Current deep learning-based approaches for cell segmentation in multiplexed images require simplifying the input to a small and fixed number...
Cancer, a pervasive global health concern, particularly affects the gastrointestinal (GI) tract, contributing to a significant portion of cancer cases worldwide. Successful treatment strategies necessitate an understanding of cancer heterogeneity, which spans both inter- and intra-tumor variability. Despite extensive research on genetic and cellular heterogeneity, morphological diversity in...
Scalable integration of high throughput open-source image analysis software to quantify pancreatic tissue remains elusive. Here we demonstrate an integration of Cellpose, Radial Symmetry-Fluorescent In Situ Hybridization (RS-FISH), and Fiji for a per cell assessment of mRNA copy number after RNA in-situ hybridization (RNAscope). Pipeline performance was tested against murine pancreata probed...
DNA's flexible structure and mechanics are intrinsically linked to its function. Damage to DNA disrupts essential processes, increasing cancer risk. However, it can be exploited in cancer therapeutics by targeting the DNA in cancer cells. The relationship between DNA damage and its mechanics is not well understood. New-generation metallodrugs offer a promising route for anticancer therapies -...
Deep learning has revolutionized instance segmentation, i.e. the precise localization of individual objects. In microscopy, the two most popular approaches, Stardist and Cellpose, are now used in routine to segment nuclei or cells. However, some specific applications might benefit from the segmentation of both nuclei and cells. For example, multi/hyperplexing imaging show cells associated with...
Light-sheet fluorescence microscopy (LSFM) or selective plane illumination microscopy (SPIM) is the method of choice for studying organ morphogenesis and function as it permits gentle and rapid volumetric imaging of biological specimens over days. In such inhomogeneous samples, however, sample-induced aberrations, including absorption, scattering, and refraction, degrade the image,...
Advancements in microscopy technologies, such as light sheet microscopy, allow life scientists to acquire data with better spatial and temporal resolution, enhancing potential insights into cellular, developmental, and stem cell biology. This has generated a need for robust computational tools to analyze large-scale image data.
We present Mastodon, a plugin for the ImageJ software, designed...
In-situ cryo electron tomography (cryo-ET) is a powerful technique to visualize cellular components in native context and near-atomic resolution. The workflow often involves the use of a focused ion beam (FIB) to thin down the vitrified cell into a ~200 nm thick “cryo lamella”. Considering the low abundance of certain subcellular structures and the extremely limited volume of the cryo lamella,...
Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN). CIN can therefore be effectively quantified by enumerating micronuclei frequency using high-content microscopy. Despite recent advancements in automation...
Microscopy image data is abundant today, but evaluating it manually is nearly impossible. For instance, to study drug-induced cell morphology changes in prenatal cardiomyocytes, researchers acquire high-resolution images of stained tissue slides. These images can contain over 10,000 cells and nuclei. However, manual evaluation is time-consuming and often lacks sufficient capacity.
To...
Hyperspectral imaging (HSI) and fluorescence lifetime imaging microscopy (FLIM) have revolutionized bioimaging by introducing new dimensions of data analysis. HSI combines imaging and spectroscopy to capture detailed fluorescence spectra, allowing simultaneous measurement of many fluorophores in a single field-of-view. FLIM provides an extra temporal resolution by measuring photon arrival...
Cell mechanics impact cell shape and drive crucial morphological changes during development and disease. However, quantifying mechanical parameters in living cells in a tissue context is challenging. Here, we introduce OptiCell3D, a computational pipeline to infer mechanical properties of cells from 3D microscopy images. OptiCell3D leverages a deformable cell model and gradient-based...
Creation of machine learning networks for biological imaging tasks often suffers from a crucial gap: the subject matter experts who understand the images well are not typically computationally comfortable training neural networks, and lack simple ways getting started to do so. We present here Piximi (piximi.app), a free, open-source "Images To Discovery" web app designed to make it easy to...
Mosquito-borne diseases pose a major global health threat, especially in tropical and subtropical regions, worsened by global warming. Mosquito species identification is vital for research, but traditional methods are costly and require expertise. Deep learning offers a promising solution, yet Convolutional Neural Network (CNN) models often perform well only in controlled environments....
Cell tracking is a key computational task in live single-cell microscopy. With the advent of automated high-throughput microscopy platforms, the amount of data quickly exceeds what humans are able to overlook. Thus, reliable and uncertainty-aware data analysis pipelines to process the collected amounts of data become crucial. In this work, we investigate the problem of quantifying uncertainty...
Cell tracking and lineage provide unique insights to study bacterial growth and dynamics. Tracking strongly relies on segmentation quality, and integrating accurate and robust segmentation algorithms is a key challenge when developing end-to-end tracking tools. Omnipose, a state of the art deep learning algorithm developed for bacteria segmentation, proved to outperform more traditional...
Although we have sequenced the entire genome, we still do not understand how many diseases evolve and progress. This is partly due to the challenge in observing the nanometre scale interactions of flexible DNA molecules, and the large conformational landscape that can encourage or inhibit some protein binding mechanisms. Our atomic force microscopy (AFM) techniques can probe biomolecular...
Recent advancements in image-based spatial RNA profiling enable to resolve tens to hundreds of distinct RNA species with high spatial resolution. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analysis, such as in-situ cell type calling. To this end, a DAPI channel is acquired, from which nuclei can be segmented by state-of-the art...
Advanced cell cultures based on human stem cells are of great interest to improve human safety and reduce, refine, and replace animal tests when evaluating substances. Human induced pluripotent stem cells (hiPSCs) can be turned into beating heart muscle cells known as cardiomyocytes, which model the early developmental stages of the embryonic heart. It is essential to reliably identify these...
Phase-contrast microscopy has become the gold standard to determine the shape and track the growth of individual bacterial cells, contributing to the identification of different cellular morphologies and to the development of models for cell size homeostasis. Although phase-contrast microscopy is well suited to the characterization of well separated, individual cells—such as those formed by...
Supervised learning algorithms for image segmentation provide exceptional results in situations where they can be applied. However, their performance diminishes when the training data is limited, or the image conditions vary considerably. Such is the case of localizing suitable acquisition points for cryo-electron tomography (CryoET): the image conditions change during screening sessions, and...
Image-based cell profiling has become a fundamental approach for understanding biological function across diverse conditions. However, as bio-imaging datasets scale, existing tooling for image-based profiling has largely been co-opted from software that initially catered to biologists running smaller, and often interactive, experimental workflows. Here, we introduce pollen, a fast and...
Single Molecule Localization Microscopy (SMLM) surpasses diffraction limit by separating signal from individual proteins in time. Although the usual resolution limit is around 20 nm for most techniques, DNA-PAINT [1] and RESI [2] achieve the resolution at the level of individual proteins. Inferring protein positions from sets of localizations is key to examining oligomeric configurations of...
Based on the NEUBIAS (bio image analysts) and COMULIS (Correlated Multimodal Imaging in Life science) community effort, we have build a data base of image analysis software, aiming to be feed and used by the community. In this workshop, we will show how to search efficiently for an existing tool in bio image analysis, how to add a new tool or ressource and how to curate an existing entry, that...
The Human Protein Atlas (HPA) is a comprehensive resource that maps the human proteome by documenting the expression and localization of proteins in human tissues, cells, and organs. It integrates various high-throughput technologies and techniques to provide detailed information about where and how proteins are expressed within the human body. Open access is one of the key features of this...
Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) have become the go-to methods for examining cellular structures in their near-native states.
However, cryo-ET, adaptable to various biological questions, often requires specific data processing adjustments, rendering a one-size-fits-all approach ineffective.
Moreover, cryo-ET is becoming increasingly complex, with more...
Measuring the quality of fluorescence microscopy images is critical for applications including automated image acquisition and image processing. Most image quality metrics used are full-reference metrics, where a test image is quantitatively compared to a reference or ‘ground truth’ image. Commonly used full-reference metrics in fluorescence microscopy include NRMSE, PSNR, SSIM and Pearson’s...
Non-gallstone pancreatitis is a painful and common inflammation of the pancreas without specific, causal treatment. Here, we study the effect of a novel treatment candidate on mouse pancreatitis. To determine treatment efficacy, we ask how inflamed and treated pancreases resemble their untreated and healthy counterparts in histopathology slices.
For this, we take two approaches: For one,...
Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixels. Current self- and unsupervised denoisers can address signal-dependent noise, but none can reliably remove noise that is also row- or column-correlated. Here, we present the first fully unsupervised deep...
We present the Virtual Embryo Zoo, an innovative web app for visualizing and sharing embryo cell tracking data. This platform empowers researchers to investigate single-cell embryogenesis of six commonly studied model organisms: Drosophila, zebrafish, C. elegans, Ascidian, mouse, and Tribolium, through an intuitive and accessible web-based interface. The Virtual Embryo Zoo viewer allows users...
The SAMJ Annotator is a new plugin for ImageJ, Fiji, and Icy that offers user-friendly access to advanced image-segmentation models. These transformer-based models, including the Segment Anything Model (SAM) variants, have shown exceptional performance in segmenting complex and heterogeneous objects in natural images.
With SAMJ Annotator, users can easily annotate objects using manual prompts...
Unlock spatial transcriptomics with our open-source pipeline, generating single-cell datasets from microscopy images. Our modular pipeline streamlines workflows by integrating image registration, segmentation, RNA peak-calling, spot-decoding and visualization steps. With our flexible framework, customize your workflow to meet project requirements. Seamlessly process data using Nextflow, ideal...
Paintera is a tool specializing in dense annotation of large 3D images. In this workshop, users will create multiscale label datasets, and learn to use the various tools and annotation modes that Paintera provides. This will include semi-automated segmentation with Segment Anything and 3D annotations with Shape Interpolation in arbitrary orientation, which can be leveraged to quickly generate...
"Lightsheet microscopy is a rapidly developing and spreading technology that now enables imaging of very large, fixed samples such as adult mouse brains at single-cell. Previously, we developed the BigStitcher software that efficiently handles and interactively reconstructs large lightsheet acquisitions up to the terabyte range. However, new types of image acquisitions use modes such as...
❗ IMPORTANT INFO FOR THE WS ❗
For the workshop, it is recommended to have the following software installed on your laptops:
• DL4MicEverywhere (https://github.com/HenriquesLab/DL4MicEverywhere). Please launch the Notebook: U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb
• Docker (DL4MicEverywhere will install it automatically the first time you launch it, but if this does not work, you can...
We will present "motile" (https://funkelab.github.io/motile/) and its napari plugin for the tracking of objects in image sequences. The plugin allows users to go from a segmentation (e.g., of cells or nuclei) to a full tracking solution (e.g., a lineage tree) within a few clicks. Motile uses integer linear programs (ILPs) to solve tracking problems and comes with many useful costs and...
QuPath is a popular open-source platform for visualizing, annotating and analyzing complex images - including whole slide and highly multiplexed datasets. It's especially useful for digital pathology applications, but can also be used for a wide range of other tasks (albeit mostly 2D... for now).
This work will introduce QuPath's main features, concepts and interface. It will show examples of...
ScaleFEx℠ is an open-source Python pipeline designed for extracting biologically relevant features from single cells in large image-based high-content imaging screens. Optimized for efficiency, it can easily handle datasets containing millions of images and several Tbs of data with reduced overhead and minimal redundancy. In our upcoming workshop, we will demonstrate how to install, configure,...
This session will provide hands-on experience with BiaPy's user-friendly workflows, zero-code notebooks, and Docker integration, empowering participants to tackle complex bioimage analysis tasks with ease. Whether you are a novice or an experienced developer, discover how BiaPy can streamline your image analysis processes and enhance your research capabilities in life sciences.
💻 Workshop...
In this workshop, we will explore CellProfiler's options for interfacing with other open-source tools, including Cellpose, ImageJ, and ilastik. We will introduce resources on creating additional CellProfiler plugins, but focus will be on the use of existing interfaces and plugins. Basic CellProfiler comfort not 100% mandatory but strongly encouraged.
Modern microscopy and other scientific data acquisition methods generate large high-dimensional datasets exceeding the size of computer main memory and often local storage
space. In this workshop, you will learn to create lazy processing workflows with ImgLib2, using the N5 API for storing and loading large n-dimensional datasets, and how to use Spark to parallelize such workflows on compute...
This workshop will provide hands-on demonstrations of non-linear image registration workflows essential for various applications in biomedical research (multi-modal imaging, thin slices alignment to 3D atlas, and cyclic IF).
We will showcase the use of imglib2-based tools such as BigWarp, Warpy, and ABBA, highlighting their capabilities in performing complex image registrations. Attendees...
This workshop we will show the latest advancements in ilastik, a user-friendly machine learning-based image analysis software that requires no prior machine learning expertise. We will explore how to work with multiscale ome-ngff data (ome-zarr), enhancing ilastik's ability to handle large and complex datasets. Additionally, we will discuss improvements in integration with segmentation tools...
The Segment Anything Model (SAM) by Meta is a transformer model trained on 11 million images and 1.1 billion masks, known for its exceptional generalization and zero-shot segmentation capabilities. In under a year, SAM has been cited in over 2,500 papers and has been widely adopted for specialized tasks like medical imaging and microscopy. This proposed course will provide hands-on guidance...
Extracting information from the acquired images is the final, necessary step to obtain quantitative information from your Microscopy experiments.
This process is known as Image Analysis and group a series of complex approaches requiring specialized personnel with solid computer science background and, usually, good programming skills. In this workshop we will introduce ZEISS arivis, the...
Join us for an engaging hands-on workshop at the Image to Knowledge Conference, where we will introduce participants to our collective efforts in the AI4Life project, a Horizon Europe-funded initiative to make AI tools accessible to the bioimage community and bridge the gap between life scientists and computational experts. This workshop brings the opportunity for participants to discover and...
OME-Zarr is a community-backed file format that is well suited for storing and sharing images. WEBKNOSSOS is an open-source tool for visualizing, annotating and sharing large multi-dimensional images. In this workshop, we will give an intro to the OME-Zarr format and provide a hands-on walkthrough the basics of WEBKNOSSOS including data upload, volume annotation (including AI-based...
During the game the players (The Scientists), team up against a game master (Reviewer#3). For a prompted biological question, the Scientists have to prepare an Experiment Plan using a deck of cards. Their plan will be reviewed and challenged by Reviewer#3 using their own deck. During this workshop attendees will play coLoↄ the board game in small teams of Scientist and the Reviewer will either...
In this interactive workshop, we will explore ultrack, a cutting-edge multi-hypothesis tracking framework designed for cell tracking across multiple microscopy modalities. Participants will begin by learning the core principles that drive ultrack's robust performance, including its ability to handle multiple input types such as intensity images, segmentation masks, contours or a combination of...
Together with the participants, we will explore the BioImage Model Zoo: an accessible, user-friendly repository of FAIR, pre-trained deep learning models. We will go through the current model collection, check models by their descriptions in cards, try them out on the website and in partner desktop tools such as ilastik and DeepImageJ. We will communicate with the AI chatbot for tips on image...
In this workshop we propose to learn the principles of phasor analysis of Fluoresce Lifetime Microscopy (FLIM) data as well as to perform image analysis of FLIM data with this approach using (F)luorescence (L)ifetime (U)l(t)imate (E)xplorer (FLUTE) a new open source and user-friendly GUI that we recently developed [1,2]. Using FLUTE, participants will learn the basis of FLIM calibration,...
The workshop aims to introduce pymmcore-plus, a Python package designed for controlling microscopes via the open-source software Micro-Manager within a pure Python environment. It is an extension of pymmcore, the original Python 3.x bindings for the C++ Micro-Manager core and, as such, it operates independently of Java, eliminating the need for Java dependencies. Throughout the workshop, we'll...
In this workshop we will go over basics of using BigTrace plugin to extract and analyze filament/vessel/neurite-like structures in large 3D multicolor (timelapse) datasets. It will be focused on volumetric dataset navigation, creation of ROIs and their analysis: measurements of intensity and geometrical properties, subvolume extraction and post-processing.
We will learn how to use conv-paint, a fast and interactive pixel classification tool for multi dimensional images. A graphical user interface is integrated into the image viewer napari, but we will also learn how to script the software from the python ecosystem. As a napari-plugin, conv-paint can easily be integrated with other plugins into complex image processing pipelines, even by users...
CAREamics is a modern re-implementation of widely used algorithms (CARE, N2N, N2V) and of more recent methods (DivNoising, HDN, muSplit, etc.), with a strong emphasis on usability for non-specialists. In this workshop, we will walk users through standard pipelines, how to approach a denoising task, which methods to choose and the various ways to use CAREamics, including via its napari UI.
This workshop will delve into the innovative features of NanoPyx, a Python framework designed to streamline microscopy image analysis. Participants will have hands-on experience on the image analysis methods implemented in NanoPyx, such as image registration, super-resolution and quality metrics, using the multiple user interfaces of NanoPyx: napari plugin, Jupyter notebooks and python...
We believe that FeatureForest addresses some of the shortcomings of both deep learning approaches and classical classifiers. While not a novelty, coupling deep-learning features and random forest allows complex biological objects to be segmented easily, without the need for extensive manual data labeling or expert deep-learning knowledge. We want to advertise the tool as it can be helpful to...
In this workshop, we would like to introduce the functionalities of MemBrain v2 and demonstrate its handling using some showcase examples.
We will introduce MemBrain v2's three main components: MemBrain-seg is our out-of-the-box U-Net that gives good segmentations on a wide variety of tomograms. MemBrain-pick closely interacts with an annotation software to enable interactive training and...
QuPath is a popular open-source platform for visualizing, annotating and analyzing complex images - including whole slide and highly multiplexed datasets. QuPath is written in Java and scriptable with Groovy, which makes it portable, powerful, and... sometimes a pain if you aren't a programmer. It can also be annoying if you are a programmer, but you find Groovy's syntax weird { and...
Based on the NEUBIAS (bio image analysts) and COMULIS (Correlated Multimodal Imaging in Life science) community effort, we have build a data base of image analysis software, aiming to be feed and used by the community. In this workshop, we will show how to search efficiently for an existing tool in bio image analysis, how to add a new tool or ressource and how to curate an existing entry,...
Measuring the quality of fluorescence microscopy images is critical for applications including automated image acquisition and image processing. Most image quality metrics used are full-reference metrics, where a test image is quantitatively compared to a reference or ‘ground truth’ image. Commonly used full-reference metrics in fluorescence microscopy include NRMSE, PSNR, SSIM and Pearson’s...
Machine learning (ML) is becoming pivotal in life science research, offering powerful tools for interpreting complex biological data. In particular, explainable ML provides insights into the reasoning behind model predictions, highlighting the data features that drove the model outcome. Our work focuses on building explainable ML models for microscopy images. These models not only classify...
Cell mechanics impact cell shape and drive crucial morphological changes during development and disease. However, quantifying mechanical parameters in living cells in a tissue context is challenging. Here, we introduce OptiCell3D, a computational pipeline to infer mechanical properties of cells from 3D microscopy images. OptiCell3D leverages a deformable cell model and gradient-based...
