232 lines
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232 lines
23 KiB
Plaintext
# Awesome Biological Image Analysis [](https://awesome.re)
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<p align="center">
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<br>
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<img width="300" src="awesome-biological-image-analysis.svg" alt="Awesome Biological Image Analysis">
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<br>
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<br>
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</p>
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> Tools and resources for biological image analysis.
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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.
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## Contents
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- [General image analysis software](#general-image-analysis-software)
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- [Image processing and segmentation](#image-processing-and-segmentation)
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- [Ecology](#ecology)
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- [Neuroscience](#neuroscience)
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- [Plant science](#plant-science)
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- [Fluoresence in situ hybridization](#fluoresence-in-situ-hybridization)
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- [Electron and super resolution microscopy](#electron-and-super-resolution-microscopy)
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- [Image restoration and quality assessment](#image-restoration-and-quality-assessment)
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- [Cell migration and particle tracking](#cell-migration-and-particle-tracking)
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- [Pathology](#pathology)
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- [Mycology](#mycology)
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- [Microbiology](#microbiology)
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- [Yeast imaging](#yeast-imaging)
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- [Other](#other)
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- [Publications](#publications)
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## General image analysis software
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- [3D Slicer](https://github.com/Slicer/Slicer) - Free, open source and multi-platform software package widely used for medical, biomedical, and related imaging research.
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- [BiaPy](https://biapyx.github.io/) - Open source ready-to-use all-in-one library that provides deep-learning workflows for a large variety of bioimage analysis tasks.
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- [BioImageXD](https://bioimagexd.net) - Free, open source software package for analyzing, processing and visualizing multi-dimensional microscopy images.
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- [Cell-ACDC](https://github.com/SchmollerLab/Cell_ACDC) - A GUI-based Python framework for segmentation, tracking, cell cycle annotations and quantification of microscopy data.
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- [CellProfiler](https://github.com/CellProfiler/CellProfiler) - Open-source software helping biologists turn images into cell measurements.
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- [CellProfiler Analyst](https://github.com/CellProfiler/CellProfiler-Analyst) - Open-source software for exploring and analyzing large, high-dimensional image-derived data.
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- [Fiji](https://github.com/fiji/fiji) - A "batteries-included" distribution of ImageJ — a popular, free scientific image processing application.
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- [Flika](https://github.com/flika-org/flika) - An interactive image processing program for biologists written in Python.
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- [Icy](https://github.com/Icy-imaging) - Open community platform for bioimage informatics, providing software resources to visualize, annotate and quantify bioimaging data.
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- [Ilastik](https://github.com/ilastik/ilastik) - Simple, user-friendly tool for interactive image classification, segmentation and analysis.
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- [ImageJ](https://github.com/imagej/ImageJ) - Public domain software for processing and analyzing scientific images.
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- [ImageJ2](https://github.com/imagej/imagej2) - A Rewrite of ImageJ for multidimensional image data, with a focus on scientific imaging.
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- [ImagePy](https://github.com/Image-Py/imagepy) - Open source image processing framework written in Python.
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- [Napari](https://github.com/napari/napari) - Fast, interactive, multi-dimensional image viewer for Python.
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- [OpenCV](https://github.com/opencv/opencv) - Open source computer vision and machine learning software library.
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- [PYME](https://github.com/python-microscopy/python-microscopy) - Open-source application suite for light microscopy acquisition, data storage, visualization, and analysis.
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- [Scikit-image](https://github.com/scikit-image/scikit-image) - Collection of algorithms for image processing.
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## Image processing and segmentation
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- [Ark-Analysis](https://github.com/angelolab/ark-analysis) - A pipeline toolbox for analyzing multiplexed imaging data.
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- [AtomAI](https://github.com/pycroscopy/atomai) - PyTorch-based package for deep/machine learning analysis of microscopy data.
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- [Cellpose](https://github.com/MouseLand/cellpose) - A generalist algorithm for cell and nucleus segmentation.
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- [CellSAM](https://github.com/vanvalenlab/cellSAM) - A foundation model for cell segmentation trained on a diverse range of cells and data types.
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- [Cellshape](https://github.com/Sentinal4D/cellshape) - 3D single-cell shape analysis of cancer cells using geometric deep learning.
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- [CLIJ2](https://clij.github.io/) - GPU-accelerated image processing library for ImageJ/Fiji, Icy, MATLAB and Java.
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- [DeepCell](https://github.com/vanvalenlab/deepcell-tf) - Deep learning library for single cell analysis.
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- [DeepSlide](https://github.com/BMIRDS/deepslide) - A sliding window framework for classification of high resolution microscopy images.
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- [EBImage](https://github.com/aoles/EBImage) - Image processing toolbox for R.
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- [GPim](https://github.com/ziatdinovmax/GPim) - Gaussian processes and Bayesian optimization for images and hyperspectral data.
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- [MAPS](https://github.com/mahmoodlab/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.
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- [MicroSAM](https://github.com/computational-cell-analytics/micro-sam) - Tools for segmentation and tracking in microscopy build on top of SegmentAnything. Segment and track objects in microscopy images interactively.
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- [MorpholibJ](https://github.com/ijpb/MorphoLibJ) - Collection of mathematical morphology methods and plugins for ImageJ.
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- [Nellie](https://github.com/aelefebv/nellie) - Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy.
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- [PartSeg](https://github.com/4DNucleome/PartSeg) - A GUI and a library for segmentation algorithms.
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- [Proseg](https://github.com/dcjones/proseg) : A cell segmentation method for in situ spatial transcriptomics.
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- [PyImSegm](https://github.com/Borda/pyImSegm) - Image segmentation - general superpixel segmentation and center detection and region growing.
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- [Salem²](https://github.com/JackieZhai/SALEM2) - Segment Anything in Light and Electron Microscopy via Membrane Guidance.
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- [Squidpy](https://github.com/scverse/squidpy) - 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.
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- [StarDist](https://github.com/stardist/stardist) - Object detection with Star-convex shapes.
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- [Suite2p](https://github.com/MouseLand/suite2p) - Pipeline for processing two-photon calcium imaging data.
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- [SyMBac](https://github.com/georgeoshardo/SyMBac) - Accurate segmentation of bacterial microscope images using synthetically generated image data.
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- [Trainable Weka Segmentation](https://github.com/fiji/Trainable_Segmentation) - Fiji plugin and library that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations.
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## Ecology
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- [PAT-GEOM](http://ianzwchan.com/my-research/pat-geom/) - A software package for the analysis of animal colour pattern.
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- [ThermImageJ](https://github.com/gtatters/ThermImageJ) - ImageJ functions and macros for working with thermal image files.
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## Neuroscience
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- [AxonDeepSeg](https://github.com/axondeepseg/axondeepseg) - Segment axon and myelin from microscopy data using deep learning.
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- [BG-atlasAPI](https://github.com/brainglobe/bg-atlasapi) - A lightweight Python module to interact with atlases for systems neuroscience.
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- [Brainreg](https://github.com/brainglobe/brainreg) - Automated 3D brain registration with support for multiple species and atlases.
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- [Brainreg-napari](https://github.com/brainglobe/brainreg-napari) - Automated 3D brain registration in napari with support for multiple species and atlases.
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- [Brainrender](https://github.com/brainglobe/brainrender) - Python package for the visualization of three dimensional neuro-anatomical data.
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- [CaImAn](https://github.com/flatironinstitute/CaImAn) - Computational toolbox for large scale Calcium Imaging Analysis.
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- [Cellfinder](https://github.com/brainglobe/cellfinder) - Automated 3D cell detection and registration of whole-brain images.
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- [Cellfinder-napari](https://github.com/brainglobe/cellfinder-napari) - Efficient cell detection in large images using [cellfinder](https://brainglobe.info/cellfinder) in napari.
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- [CloudVolume](https://github.com/seung-lab/cloud-volume) - Read and write Neuroglancer datasets programmatically.
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- [NeuroAnatomy Toolbox](https://github.com/natverse/nat) - R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons.
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- [Neuroglancer](https://github.com/google/neuroglancer/) - WebGL-based viewer for volumetric data.
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- [NeuronJ](https://imagescience.org/meijering/software/neuronj/) - An ImageJ plugin for neurite tracing and analysis.
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- [Panda](https://www.nitrc.org/projects/panda/) - Pipeline for Analyzing braiN Diffusion imAges: A MATLAB toolbox for pipeline processing of diffusion MRI images.
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- [PyTorch Connectomics](https://github.com/zudi-lin/pytorch_connectomics) - Deep learning framework for automatic and semi-automatic annotation of connectomics datasets, powered by PyTorch.
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- [RivuletPy](https://github.com/RivuletStudio/rivuletpy) - Robust 3D Neuron Tracing / General 3D tree structure extraction in Python for 3D images powered by the Rivulet2 algorithm.
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- [SNT](https://github.com/morphonets/SNT/) - ImageJ framework for semi-automated tracing and analysis of neurons.
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- [TrailMap](https://github.com/AlbertPun/TRAILMAP/) - Software package to extract axonal data from cleared brains.
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- [Wholebrain](https://github.com/tractatus/wholebrain) - Automated cell detection and registration of whole-brain images with plot of cell counts per region and Hemishpere.
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- [ZVQ - Zebrafish Vascular Quantification](https://github.com/ElisabethKugler/ZFVascularQuantification) - Image analysis pipeline to perform 3D quantification of the total or regional zebrafish brain vasculature using the image analysis software Fiji.
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## Plant science
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- [Aradeepopsis](https://github.com/Gregor-Mendel-Institute/aradeepopsis) - A versatile, fully open-source pipeline to extract phenotypic measurements from plant images.
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- [DIRT](https://github.com/Computational-Plant-Science/DIRT) - Digital Imaging of Root Traits: Extract trait measurements from images of monocot and dicot roots.
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- [LeafByte](https://zoegp.science/leafbyte) - Free and open source mobile app for measuring herbivory quickly and accurately.
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- [PaCeQuant](https://mitobo.informatik.uni-halle.de/index.php/Applications/PaCeQuant) - An ImageJ-based tool which provides a fully automatic image analysis workflow for PC shape quantification.
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- [PhenotyperCV](https://github.com/jberry47/ddpsc_phenotypercv) - Header-only C++11 library using OpenCV for high-throughput image-based plant phenotyping.
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- [PlantCV](https://github.com/danforthcenter/plantcv) - Open-source image analysis software package targeted for plant phenotyping.
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- [PlantSeg](https://github.com/hci-unihd/plant-seg) - Tool for cell instance aware segmentation in densely packed 3D volumetric images.
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- [RhizoTrak](https://prbio-hub.github.io/rhizoTrak/) - Open source tool for flexible and efficient manual annotation of complex time-series minirhizotron images.
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- [Rhizovision Explorer](https://github.com/rootphenomicslab/RhizoVisionExplorer) - Free and open-source software developed for estimating root traits from images acquired from a flatbed scanner or camera.
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- [RootPainter](https://github.com/Abe404/root_painter) - Deep learning segmentation of biological images with corrective annotation.
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## Fluoresence in situ hybridization
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- [Big-fish](https://github.com/fish-quant/big-fish) - Python package for the analysis of smFISH images.
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- [DypFISH](https://github.com/cbib/dypfish) - Python library for spatial analysis of smFISH images.
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- [RS-FISH](https://github.com/PreibischLab/RS-FISH) - Fiji plugin to detect FISH spots in 2D/3D images which scales to very large images.
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- [Spotiflow](https://github.com/weigertlab/spotiflow) - A deep learning-based, threshold-agnostic, and subpixel-accurate spot detection method developed for spatial transcriptomics workflows.
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- [TissUUmaps](https://tissuumaps.github.io/) - Visualizer of NGS data, plot millions of points and interact, gate, export. ISS rounds and base visualization.
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## Electron and super resolution microscopy
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- [ASI_MTF](https://github.com/emx77/ASI_MTF) - ImageJ macro to calculate the modulation transfer function (MTF) based on a knife edge (or slanted edge) measurement.
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- [DECODE](https://github.com/TuragaLab/DECODE) - Python and PyTorch based deep learning tool for single molecule localization microscopy.
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- [Empanada](https://github.com/volume-em/empanada) - Panoptic segmentation algorithms for 2D and 3D electron microscopy images.
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- [Em-scalebartools](https://github.com/lukmuk/em-scalebartools) - Fiji/ImageJ macros to quickly add a scale bar to an (electron microscopy) image.
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- [Picasso](https://github.com/jungmannlab/picasso) - A collection of tools for painting super-resolution images.
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- [SMAP](https://github.com/jries/SMAP) - A modular super-resolution microscopy analysis platform for SMLM data.
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- [ThunderSTORM](https://github.com/zitmen/thunderstorm) - A comprehensive ImageJ plugin for SMLM data analysis and super-resolution imaging.
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## Image restoration and quality assessment
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- [CSBDeep](https://github.com/CSBDeep/CSBDeep) - A deep learning toolbox for microscopy image restoration and analysis.
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- [Ijp-color](https://github.com/ij-plugins/ijp-color) - Plugins for ImageJ - color space conversions and color calibration.
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- [Image Quality](https://github.com/ocampor/image-quality) - Open source software library for Image Quality Assessment (IQA).
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- [LLSpy](https://github.com/tlambert03/LLSpy) - Python library to facilitate lattice light sheet data processing.
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- [NCS](https://github.com/HuanglabPurdue/NCS) - Noise correction algorithm for sCMOS cameras.
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- [Noise2Void](https://github.com/juglab/n2v) - Learning denoising from single noisy images.
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## Cell migration and particle tracking
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- [CellMigration](https://github.com/quantixed/CellMigration) - Analysis of 2D cell migration in Igor.
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- [TrackMate](https://github.com/fiji/TrackMate) - User-friendly interface that allows for performing tracking, data visualization, editing results and track analysis in a convenient way.
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- [TrackMateR](https://github.com/quantixed/TrackMateR) - R package to analyze cell migration and particle tracking experiments using outputs from TrackMate.
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- [Trackpy](https://soft-matter.github.io/trackpy) - Fast and Flexible Particle-Tracking Toolkit.
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- [TracX](https://gitlab.com/csb.ethz/tracx) - MATLAB generic toolbox for cell tracking from various microscopy image modalities such as Bright-field (BF), phase contrast (PhC) or fluorescence (FL) with an automated track quality assessment in
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absence of a ground truth.
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- [TraJClassifier](https://imagej.net/plugins/trajclassifier) - Fiji plugin that loads trajectories from TrackMate, characterizes them using TraJ and classifiies them into normal diffusion, subdiffusion, confined diffusion and directed/active motion by a random forest approach (through Renjin).
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- [QuimP](https://github.com/CellDynamics/QuimP) - Software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane.
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- [Ultrack](https://github.com/royerlab/ultrack) - Versatile cell tracking method for 2D, 3D, and multichannel timelapses, overcoming segmentation challenges in complex tissues.
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- [Usiigaci](https://github.com/oist/usiigaci) - Stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning.
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## Pathology
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- [FastPathology](https://github.com/AICAN-Research/FAST-Pathology) - Open-source software for deep learning-based digital pathology.
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- [HistoClean](https://github.com/HistoCleanQUB/HistoClean) - Tool for the preprocessing and augmentation of images used in deep learning models.
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- [Minerva](https://github.com/labsyspharm/minerva-story) - Image viewer designed specifically to make it easy for non-expert users to interact with complex tissue images.
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- [Orbit](http://www.orbit.bio) - A versatile image analysis software for biological image-based quantification using machine learning, especially for whole slide imaging.
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- [PathML](https://github.com/Dana-Farber-AIOS/pathml) - An open-source toolkit for computational pathology and machine learning.
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- [PAQUO](https://github.com/bayer-science-for-a-better-life/paquo) - A library for interacting with QuPath from Python.
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- [QuPath](https://qupath.github.io/) - Open source software for digital pathology image analysis.
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## Mycology
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- [DeepMushroom](https://github.com/Olament/DeepMushroom) - Image classification of fungus using ResNet.
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- [Fungal Feature Tracker (FFT)](https://github.com/hsueh-lab/FFT) - Tool to quantitatively characterize morphology and growth of filamentous fungi.
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## Microbiology
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- [BactMap](https://github.com/vrrenske/BactMAP) - A command-line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots.
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- [BacStalk](https://drescherlab.org/data/bacstalk/docs/index.html) - Interactive and user-friendly image analysis software tool to investigate the cell biology of common used bacterial species.
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- [BiofilmQ](https://drescherlab.org/data/biofilmQ/docs/) - Advanced biofilm analysis tool for quantifying the properties of cells inside large 3-dimensional biofilm communities in space and time.
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## Yeast imaging
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- [BABY](https://git.ecdf.ed.ac.uk/swain-lab/baby/) - An image processing pipeline for accurate single-cell growth estimation of
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budding cells from bright-field stacks.
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- [htsimaging](https://github.com/rraadd88/htsimaging) - Python package for high-throughput single-cell imaging analysis.
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- [YeastMate](https://yeastmate.readthedocs.io/en/latest/) - Neural network-assisted segmentation of mating and budding events in S. cerevisiae.
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- [YeaZ](https://github.com/lpbsscientist/YeaZ-GUI) - An interactive tool for segmenting yeast cells using deep learning.
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## Other
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- [AICSImageIO](https://github.com/AllenCellModeling/aicsimageio) - Image reading, metadata conversion, and image writing for nicroscopy images in Python.
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- [Biobeam](https://maweigert.github.io/biobeam) - 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.
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- [BoneJ](https://github.com/bonej-org/BoneJ2) - Collection of Fiji/ImageJ plug-ins for skeletal biology.
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- [CaPTk](https://github.com/CBICA/CaPTk) - Cancer Imaging Phenomics Toolkit: A software platform to perform image analysis and predictive modeling tasks.
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- [ColiCoords](https://github.com/Jhsmit/ColiCoords) - Python project for analysis of fluorescence microscopy data from rodlike cells.
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- [CompactionAnalyzer](https://github.com/davidbhr/CompactionAnalyzer) - 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.
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- [Cytominer-database](https://github.com/cytomining/cytominer-database) - Command-line tools for organizing measurements extracted from images.
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- [DetecDiv](https://github.com/gcharvin/DetecDiv) - Comprehensive set of tools to analyze time microscopy images using deep learning methods.
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- [MIA](https://github.com/mianalysis/mia) - Fiji plugin which provides a modular framework for assembling image and object analysis workflows.
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- [MorphoGraphX](https://morphographx.org) - Open source application for the visualization and analysis of 4D biological datasets.
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- [Napari-aicsimageio](https://github.com/AllenCellModeling/napari-aicsimageio) - Multiple file format reading directly into napari using pure Python.
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- [NEFI2](https://github.com/05dirnbe/nefi) - Python tool created to extract networks from images.
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- [Neurite](https://github.com/adalca/neurite) - Neural networks toolbox focused on medical image analysis.
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- [Nd2reader](https://github.com/Open-Science-Tools/nd2reader) - A pure-Python package that reads images produced by NIS Elements 4.0+.
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- [OAD](https://github.com/zeiss-microscopy/OAD) - Collection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools.
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- [Pycytominer](https://github.com/cytomining/pycytominer) - Data processing functions for profiling perturbations.
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- [Pyotf](https://github.com/david-hoffman/pyotf) - A simulation software package for modelling optical transfer functions (OTF)/point spread functions (PSF) of optical microscopes written in Python.
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- [PyScratch](https://bitbucket.org/vladgaal/pyscratch_public.git/src) - Open source tool that autonomously performs quantitative analysis of in vitro scratch assays.
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- [Quanfima](https://github.com/rshkarin/quanfima) - 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.
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- [SimpleElastix](https://github.com/SuperElastix/SimpleElastix) - Multi-lingual medical image registration library.
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- [Vaa3D](https://alleninstitute.org/what-we-do/brain-science/research/products-tools/vaa3d/) - Open-source software for 3D/4D/5D image visualization and analysis.
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- [XitoSBML](https://github.com/spatialsimulator/XitoSBML) - ImageJ plugin which creates a Spatial SBML model from segmented images.
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- [Z-stack Depth Color Code](https://github.com/ekatrukha/ZstackDepthColorCode) - ImageJ/Fiji plugin to colorcode Z-stacks/hyperstacks.
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- [ZeroCostDL4Mic](https://github.com/HenriquesLab/ZeroCostDL4Mic) - Google Colab to develop a free and open-source toolbox for deep-Learning in microscopy.
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- [ZetaStitcher](https://github.com/lens-biophotonics/ZetaStitcher) - Tool designed to stitch large volumetric images such as those produced by light-sheet fluorescence microscopes.
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## Publications
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- [A Hitchhiker's guide through the bio-image analysis software universe](https://febs.onlinelibrary.wiley.com/doi/10.1002/1873-3468.14451) - An article presenting a curated guide and glossary of bio-image analysis terms and tools.
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- [Biological imaging software tools](https://dx.doi.org/10.1038%2Fnmeth.2084) - The steps of biological image analysis and the appropriate tools for each step.
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- [Data-analysis strategies for image-based cell profiling](https://doi.org/10.1038/nmeth.4397) - In-detail explanations of image analysis pipelines.
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- [Large-scale image-based screening and profiling of cellular phenotypes](https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.22909) - A workflow for phenotype extraction from high throughput imaging experiments.
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- [Workflow and metrics for image quality control in large-scale high-content screens](https://linkinghub.elsevier.com/retrieve/pii/S2472555222075943) - Approaches for quality control in high-content imaging screens.
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## Footnotes
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### Similar lists and repositories
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- [BIII](https://biii.eu) - Repository of bioimage analysis tools.
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- [Bio-image Analysis Notebooks](https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html) - Notebooks for bioimage analysis in Python.
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- [Bioimaging Guide](https://www.bioimagingguide.org) - Microscopy for beginners reference guide.
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- [Cytodata](https://github.com/cytodata/awesome-cytodata) - A curated list of awesome cytodata resources.
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- [Napari hub](https://www.napari-hub.org) - Collection of napari plugins.
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- [OpenMicroscopy](https://github.com/HohlbeinLab/OpenMicroscopy) - Non-comprehensive list of projects and resources related to open microscopy.
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[biologicalimageanalysis.md Github](https://github.com/hallvaaw/awesome-biological-image-analysis
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)
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