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LaueMatching is a high-throughput algorithm for indexing Laue diffraction patterns. It uses direct pattern correlation by comparing pre-processed experimental images against a pre-computed library of simulated diffraction patterns to determine crystal orientation and structure.

Tapenade is a quantitative pipeline for analyzing multi-layered organoids, enabling deep imaging and quantification of cell-scale properties across tissue scales. It provides a user-friendly Python package and napari plugins for 3D nuclei segmentation and gene expression analysis.

MoEGP is a novel deep-learning network utilizing a Mixture of Experts framework for efficient and robust genomic prediction in crop breeding. It processes data features using multiple specialized experts and weights their predictions to improve accuracy across various morphological and yield traits.

caactus is a Python-based workflow that automates the analysis and counting of fungal cells from microscopy images. It provides a user-friendly, high-throughput solution for determining cell developmental stages and measuring cell sizes.

A cross-platform GUI software for automatically generating and analyzing kymographs of membrane-cortex associated proteins or lipids from live-cell time-lapse fluorescent imaging datasets. It automates complex boundary tracking and spatiotemporal analysis of cell mechanics.

SUITPy is a Python-based toolbox designed for analyzing cerebellar functional and anatomical imaging data across the human lifespan. It provides tools for automatic isolation and precise normalization of cerebellar structures.

DySTrack is an open-source Python tool designed for smart microscopy, enabling automated feedback control to track dynamic biological samples, such as migrating cells or developing tissues. It serves as a modular bridge connecting commercial microscope acquisition software to advanced Python image analysis pipelines.

ProteoGyver is a fast, user-friendly, and open-source platform for routine quality control and preliminary analysis of complex MS-based proteomics and interactomics data.

Deep Learning Enabled Accurate Segmentation of Budding Yeast Cells in Bright-field Microscopy. YeastNet: Deep Learning Semantic Segmentation for Budding Yeast Single Cell Analysis. YeastNet is a library for analysing live-cell fluorescence microscopy of budding yeast cells, written in Python3 using PyTorch 1.0.

spatialproteomics is a Python package for analyzing highly multiplexed immunofluorescence imaging data, offering end-to-end coverage of the workflow with flexibility and automation. It processes images larger than working memory and supports synchronization of shared coordinates across data modalities.

A tool for Traction Force and Monolayer Stress Microscopy. pyTFM is a python package that allows you to analyze force generation and stresses in cells, cell colonies and confluent cell layers growing on a 2 dimensional surface. This package implements the procedures of Traction Force Microscopy and Monolayer Stress Microscopy. In addition to the standard measures for stress and force generation, it also includes the line tension, a measure for the force transfer exclusively across cell-cell boundaries. pyTFM includes an addon for the image annotation tool clickpoints allowing you to quickly analyze and vizualize large datasets.

A tool to automatically quantify Point-Of-Care Tests from images. Lateral flow Point-Of-Care Tests (POCTs) are a valuable tool for rapidly detecting pathogens and the associated immune response in humans and animals. In the context of the SARS-CoV-2 pandemic, they offer rapid on-site diagnostics and can relieve centralized laboratory testing sites, thus freeing resources that can be focused on especially vulnerable groups. However, visual interpretation of the POCT test lines is subjective, error prone and only qualitative. Here we present pyPOCQuant, an open-source tool implemented in Python 3 that can robustly and reproducibly analyze POCTs from digital images and return an unbiased and quantitative measurement of the POCT test lines

MotilA is a Python pipeline for quantifying microglial fine process motility from 3D time-lapse microscopy data. It performs image preprocessing, segmentation, and motility quantification, yielding biologically interpretable metrics and is suitable for various imaging applications.

An open-source multiplexed image t-SNE viewer.

Python-based bespoke microscopy for bio-medical science. Cockpit is a microscope graphical user interface. It is a flexible and easy to extend platform aimed at life scientists using bespoke microscopes. More detailed information is available:. Micron Advanced Imaging Consortium. Micron Advanced Imaging ConsortiumMicron Advanced Imaging ConsortiumMicron Advanced Imaging Consortium.

An easy-to-use deep learning pipeline for image segmentation and classification. InstandDL: An easy and convenient deep learning pipeline for image segmentation and classification. InstantDL enables experts and non-experts to use state-of-the art deep learning methods on biomedical image data. InstantDL offers the four most common tasks in medical image processing: Semantic segmentation, instance segmentation, pixel-wise regression and classification. For more in depth discussion on the methods, as well as comparing the results and bechmarks using this package, please refer to our preprint on bioRxiv here.

ILEE is an anguided algorithm for accurate and automated quantitative analysis of cytoskeletal images.

flatbug is a powerful and general model for counting and outlining insects and other terrestrial arthropods in images. The tool provides a ready-to-use model and accompanying dataset for arthropod image detection and offers a benchmark for future research.

FishFeats is an open-source Napari plugin for streamlined quantification of multimodal labeling at the single-cell level in 3D tissues. It provides a user-friendly pipeline for integrating image analysis steps and extracting quantitative data without coding.

Cheetah is a simple to use Python library to support the development of analysis workflows and real-time cybergenetic control platforms that combine microscopy imaging and environmental control. The toolkit relies on a U-Net convolutional neural network to perform cell segmentation and uses this with additional functions that can label, track and generate control signals based on this information.

Inferring Chromosome Radial Organization from Hi-C Data. The included Python scripts (mat2net.py, and predictCTpos.py) contain an implemention of the HIRAC computational technique described in the following manuscript.

A general framework for interpreting wide-band neural activity. DeepInsight is a toolbox for the analysis and interpretation of wide-band neural activity and can be applied on unsorted neural data. This means the traditional step of spike-sorting can be omitted and the raw data can be used directly as input, providing a more objective way of measuring decoding performance.

A 3D Image Segmentation and Parent to Child relation plugin for microscopy image analysis in napari

Microscope is a Python package for the control of automated microscopes. It is easy to use and especially well suited for complex microscopes with hard timing requirements. It is aimed at those that are building their own microscopes or want programmatic control for microscope experiments.

A Fast and Robust Single-Cell Tracking Method Using Deep-Learning Mediated Cell Cycle Profiling.

An Open Source Standalone Deep Learning Application for Microscopic Image Analysis.

A Python Package of Image Analysis for Interactive Grain Counting and Measuring. Grain characteristics are critical component traits for grain yield, including kernel length, kernel width, and thousand kernel weight.

YeastMate is a tool for the automated detection and segmentation of S. cerevisae cells and their mating and budding events, as well as a subclassification of the cells involved in these events into mother and daughter cells.

Spotiflow is a deep learning method for accurate spot detection in microscopy images, particularly useful for spatial transcriptomics. It uses stereographic flow regression to achieve subpixel-accurate localizations and is available as a Python library and a napari plugin.

an open-source tool for exploring 3D super-resolution data. Localization-based super-resolution microscopy techniques such as PALM, STORM, and PAINT are increasingly critical tools for biological discovery. These methods generate lists of single fluorophore positions that capture nanoscale structural details of subcellular organisation, but to develop biological insight, we must post-process and visualize this data in a meaningful way. A large number of algorithms have been developed for localization post-processing, transforming point data into representations which approximate traditional microscopy images, and performing specific quantitative analysis directly on points. Implementations of these algorithms typically stand in isolation, necessitating complex workflows involving multiple different software packages

EPySeg is a package for segmenting 2D epithelial tissues. EPySeg also ships with a graphical user interface that allows for building, training and running deep learning models.

The bardensr (barcode dexmixing through non-negative spatial regression) package is a collection of tools for modeling linearly multiplexed observations. There is some true signal we are interested in, but our observations are some noisy linear mixture of those signals.

RS-FISH is a tool for precise, interactive and scalable single-molecule FISH spot detection using Radial Symmetry.

A Platform for Structural Variant Visual Validation and Automated Filtering. samplot is a command line tool for rapid, multi-sample structural variant visualization. samplot takes SV coordinates and bam files and produces high-quality images that highlight any alignment and depth signals that substantiate the SV. 1000 genomes high coverage crams.

Pancreatlas is applying an adaptable framework to map the human pancreas in health and disease.

A GUI-based Python framework for segmentation, tracking and cell cycle annotations of microscopy data.

A convolutional neural network for highly accurate, label-free segmentation of yeast microscopy images. Laboratory of the Physics of Biological Systems ‐ EPFL.

OpenCell is a tool for proteome-scale endogenous tagging enables the cartography of human cellular organization.