Awesome Biological Image Analysis
Tools and resources for biological image analysis.
Biological image analysis aims to increase our understanding of biology through the use of various computational techniques and approaches to obtain valuable information from images.
Contents
- General image analysis software
- Image processing and segmentation
- Ecology
- Neuroscience
- Plant science
- Fluoresence in situ hybridization
- Electron and super resolution microscopy
- Image restoration and quality assessment
- Cell migration and particle tracking
- Pathology
- Mycology
- Microbiology
- Yeast imaging
- Other
- Publications
General image analysis software
- 3D Slicer - Free, open source and multi-platform software package widely used for medical, biomedical, and related imaging research.
- BiaPy - Open source ready-to-use all-in-one library that provides deep-learning workflows for a large variety of bioimage analysis tasks.
- BioImageXD - Free, open source software package for analyzing, processing and visualizing multi-dimensional microscopy images.
- Cell-ACDC - A GUI-based Python framework for segmentation, tracking, cell cycle annotations and quantification of microscopy data.
- CellProfiler - Open-source software helping biologists turn images into cell measurements.
- CellProfiler Analyst - Open-source software for exploring and analyzing large, high-dimensional image-derived data.
- Fiji - A "batteries-included" distribution of ImageJ — a popular, free scientific image processing application.
- Flika - An interactive image processing program for biologists written in Python.
- Icy - Open community platform for bioimage informatics, providing software resources to visualize, annotate and quantify bioimaging data.
- Ilastik - Simple, user-friendly tool for interactive image classification, segmentation and analysis.
- ImageJ - Public domain software for processing and analyzing scientific images.
- ImageJ2 - A Rewrite of ImageJ for multidimensional image data, with a focus on scientific imaging.
- ImagePy - Open source image processing framework written in Python.
- Napari - Fast, interactive, multi-dimensional image viewer for Python.
- OpenCV - Open source computer vision and machine learning software library.
- PYME - Open-source application suite for light microscopy acquisition, data storage, visualization, and analysis.
- Scikit-image - Collection of algorithms for image processing.
Image processing and segmentation
- Ark-Analysis - A pipeline toolbox for analyzing multiplexed imaging data.
- AtomAI - PyTorch-based package for deep/machine learning analysis of microscopy data.
- Cellpose - A generalist algorithm for cell and nucleus segmentation.
- CellSAM - A foundation model for cell segmentation trained on a diverse range of cells and data types.
- Cellshape - 3D single-cell shape analysis of cancer cells using geometric deep learning.
- CLIJ2 - GPU-accelerated image processing library for ImageJ/Fiji, Icy, MATLAB and Java.
- DeepCell - Deep learning library for single cell analysis.
- DeepSlide - A sliding window framework for classification of high resolution microscopy images.
- EBImage - Image processing toolbox for R.
- GPim - Gaussian processes and Bayesian optimization for images and hyperspectral data.
- MAPS - 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.