Biological Image Analysis

Free, open source and multi-platform software package widely used for medical, biomedical, and related imaging research.

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

Open-source software helping biologists turn images into cell measurements.

Open-source software for exploring and analyzing large, high-dimensional image-derived data.

A "batteries-included" distribution of ImageJ — a popular, free scientific image processing application.

An interactive image processing program for biologists written in Python.

Simple, user-friendly tool for interactive image classification, segmentation and analysis.

Public domain software for processing and analyzing scientific images.

A Rewrite of ImageJ for multidimensional image data, with a focus on scientific imaging.

Open source image processing framework written in Python.

Fast, interactive, multi-dimensional image viewer for Python.

Open source computer vision and machine learning software library.

Open-source application suite for light microscopy acquisition, data storage, visualization, and analysis.

Collection of algorithms for image processing.

A pipeline toolbox for analyzing multiplexed imaging data.

PyTorch-based package for deep/machine learning analysis of microscopy data.

A generalist algorithm for cell and nucleus segmentation.

A foundation model for cell segmentation trained on a diverse range of cells and data types.

3D single-cell shape analysis of cancer cells using geometric deep learning.

A framework for lightweight cell segmentation model training and inference.

A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration.

Deep learning library for single cell analysis.

A sliding window framework for classification of high resolution microscopy images.

High-performance spatial transcriptomics deconvolution for cell type mapping using structure-preserving randomized sketching.

Gaussian processes and Bayesian optimization for images and hyperspectral data.

A multi-branch network for nuclear instance segmentation and classification with pre-trained weights.

MAPS (Machine learning for Analysis of Proteomics in Spatial biology) is a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data.

Tools for segmentation and tracking in microscopy build on top of SegmentAnything. Segment and track objects in microscopy images interactively.

Collection of mathematical morphology methods and plugins for ImageJ.

Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy.

A GUI and a library for segmentation algorithms.

A cell segmentation method for in situ spatial transcriptomics.

Image segmentation - general superpixel segmentation and center detection and region growing.

Segment Anything in Light and Electron Microscopy via Membrane Guidance.

Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins.

Object detection with spline curves.

Object detection with Star-convex shapes.

Pipeline for processing two-photon calcium imaging data.

Accurate segmentation of bacterial microscope images using synthetically generated image data.

Fiji plugin and library that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations.

ImageJ functions and macros for working with thermal image files.

Segment axon and myelin from microscopy data using deep learning.

A lightweight Python module to interact with atlases for systems neuroscience.

Automated 3D brain registration with support for multiple species and atlases.

Python package for the visualization of three dimensional neuro-anatomical data.

Computational toolbox for large scale Calcium Imaging Analysis.

Automated 3D cell detection and registration of whole-brain images.

Read and write Neuroglancer datasets programmatically.

R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons.

WebGL-based viewer for volumetric data.

Deep learning framework for automatic and semi-automatic annotation of connectomics datasets, powered by PyTorch.

ImageJ framework for semi-automated tracing and analysis of neurons.

Software package to extract axonal data from cleared brains.

Automated cell detection and registration of whole-brain images with plot of cell counts per region and Hemishpere.

Image analysis pipeline to perform 3D quantification of the total or regional zebrafish brain vasculature using the image analysis software Fiji.

A versatile, fully open-source pipeline to extract phenotypic measurements from plant images.

Header-only C++11 library using OpenCV for high-throughput image-based plant phenotyping.

Open-source image analysis software package targeted for plant phenotyping.

Tool for cell instance aware segmentation in densely packed 3D volumetric images.

Deep learning segmentation of biological images with corrective annotation.

Python package for the analysis of smFISH images.

Python library for spatial analysis of smFISH images.

Fiji plugin to detect FISH spots in 2D/3D images which scales to very large images.

A deep learning-based, threshold-agnostic, and subpixel-accurate spot detection method developed for spatial transcriptomics workflows.

ImageJ macro to calculate the modulation transfer function (MTF) based on a knife edge (or slanted edge) measurement.

Panoptic segmentation algorithms for 2D and 3D electron microscopy images.

Fiji/ImageJ macros to quickly add a scale bar to an (electron microscopy) image.

A collection of tools for painting super-resolution images.

A modular super-resolution microscopy analysis platform for SMLM data.

A comprehensive ImageJ plugin for SMLM data analysis and super-resolution imaging.

A deep learning toolbox for microscopy image restoration and analysis.

Plugins for ImageJ - color space conversions and color calibration.

Open source software library for Image Quality Assessment (IQA).

Python library to facilitate lattice light sheet data processing.

Noise correction algorithm for sCMOS cameras.

Analysis of 2D cell migration in Igor.

User-friendly interface that allows for performing tracking, data visualization, editing results and track analysis in a convenient way.

R package to analyze cell migration and particle tracking experiments using outputs from TrackMate.

Software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane.

Versatile cell tracking method for 2D, 3D, and multichannel timelapses, overcoming segmentation challenges in complex tissues.

Stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning.

Open-source software for deep learning-based digital pathology.

Tool for the preprocessing and augmentation of images used in deep learning models.

Image viewer designed specifically to make it easy for non-expert users to interact with complex tissue images.

An open-source toolkit for computational pathology and machine learning.

A library for interacting with QuPath from Python.

Image classification of fungus using ResNet.

Tool to quantitatively characterize morphology and growth of filamentous fungi.

A command-line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots.

Python package for high-throughput single-cell imaging analysis.

An interactive tool for segmenting yeast cells using deep learning.

Image reading, metadata conversion, and image writing for nicroscopy images in Python.

Collection of Fiji/ImageJ plug-ins for skeletal biology.

Cancer Imaging Phenomics Toolkit: A software platform to perform image analysis and predictive modeling tasks.

Python project for analysis of fluorescence microscopy data from rodlike cells.

Python package to quantify the tissue compaction (as a measure of the contractile strength) generated by cells or multicellular spheroids that are embedded in fiber materials.

Command-line tools for organizing measurements extracted from images.

Comprehensive set of tools to analyze time microscopy images using deep learning methods.

Fiji plugin which provides a modular framework for assembling image and object analysis workflows.

Multiple file format reading directly into napari using pure Python.

Python tool created to extract networks from images.

Neural networks toolbox focused on medical image analysis.

A pure-Python package that reads images produced by NIS Elements 4.0+.

Collection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools.

Data processing functions for profiling perturbations.

A simulation software package for modelling optical transfer functions (OTF)/point spread functions (PSF) of optical microscopes written in Python.

Quantitative Analysis of Fibrous Materials: A collection of useful functions for morphological analysis and visualization of 2D/3D data from various areas of material science.

Open-source multi-dimensional image analysis in Python, R, Java, C#, Lua, Ruby, TCL and C++.

ImageJ plugin which creates a Spatial SBML model from segmented images.

ImageJ/Fiji plugin to colorcode Z-stacks/hyperstacks.

Google Colab to develop a free and open-source toolbox for deep-Learning in microscopy.

Tool designed to stitch large volumetric images such as those produced by light-sheet fluorescence microscopes.

A curated list of awesome cytodata resources.