awesome-biological-image-analysis

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

Open source ready-to-use all-in-one library that provides deep-learning workflows for a large variety of bioimage analysis tasks.

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 article presenting a curated guide and glossary of bio-image analysis terms and tools.

The steps of biological image analysis and the appropriate tools for each step.

In-detail explanations of image analysis pipelines.

A workflow for phenotype extraction from high throughput imaging experiments.

Approaches for quality control in high-content imaging screens.

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

Open source software package that is designed to provide fast methods for in-silico optical experiments with an emphasize on image formation in biological tissues.

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.

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

Free and open source mobile app for measuring herbivory quickly and accurately.

An ImageJ-based tool which provides a fully automatic image analysis workflow for PC shape quantification.

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.

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.

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.

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.

An image processing pipeline for accurate single-cell growth estimation of budding cells from bright-field stacks.

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

Neural network-assisted segmentation of mating and budding events in S. cerevisiae.

An interactive tool for segmenting yeast cells using deep learning.