550 lines
27 KiB
HTML
550 lines
27 KiB
HTML
<h1 id="awesome-biological-image-analysis-awesome">Awesome Biological
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Image Analysis <a href="https://awesome.re"><img
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src="https://awesome.re/badge.svg" alt="Awesome" /></a></h1>
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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> <br>
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</p>
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<blockquote>
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<p>Tools and resources for biological image analysis.</p>
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</blockquote>
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<p>Biological image analysis aims to increase our understanding of
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biology through the use of various computational techniques and
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approaches to obtain valuable information from images.</p>
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<h2 id="contents">Contents</h2>
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<ul>
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<li><a href="#general-image-analysis-software">General image analysis
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software</a></li>
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<li><a href="#image-processing-and-segmentation">Image processing and
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segmentation</a></li>
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<li><a href="#ecology">Ecology</a></li>
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<li><a href="#neuroscience">Neuroscience</a></li>
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<li><a href="#plant-science">Plant science</a></li>
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<li><a href="#fluoresence-in-situ-hybridization">Fluoresence in situ
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hybridization</a></li>
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<li><a href="#electron-and-super-resolution-microscopy">Electron and
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super resolution microscopy</a></li>
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<li><a href="#image-restoration-and-quality-assessment">Image
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restoration and quality assessment</a></li>
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<li><a href="#cell-migration-and-particle-tracking">Cell migration and
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particle tracking</a></li>
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<li><a href="#pathology">Pathology</a></li>
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<li><a href="#mycology">Mycology</a></li>
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<li><a href="#microbiology">Microbiology</a></li>
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<li><a href="#yeast-imaging">Yeast imaging</a></li>
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<li><a href="#other">Other</a></li>
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<li><a href="#publications">Publications</a></li>
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</ul>
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<h2 id="general-image-analysis-software">General image analysis
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software</h2>
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<ul>
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<li><a href="https://github.com/Slicer/Slicer">3D Slicer</a> - Free,
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open source and multi-platform software package widely used for medical,
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biomedical, and related imaging research.</li>
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<li><a href="https://biapyx.github.io/">BiaPy</a> - Open source
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ready-to-use all-in-one library that provides deep-learning workflows
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for a large variety of bioimage analysis tasks.</li>
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<li><a href="https://bioimagexd.net">BioImageXD</a> - Free, open source
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software package for analyzing, processing and visualizing
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multi-dimensional microscopy images.</li>
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<li><a href="https://github.com/SchmollerLab/Cell_ACDC">Cell-ACDC</a> -
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A GUI-based Python framework for segmentation, tracking, cell cycle
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annotations and quantification of microscopy data.</li>
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<li><a
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href="https://github.com/CellProfiler/CellProfiler">CellProfiler</a> -
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Open-source software helping biologists turn images into cell
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measurements.</li>
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<li><a
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href="https://github.com/CellProfiler/CellProfiler-Analyst">CellProfiler
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Analyst</a> - Open-source software for exploring and analyzing large,
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high-dimensional image-derived data.</li>
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<li><a href="https://github.com/fiji/fiji">Fiji</a> - A
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“batteries-included” distribution of ImageJ — a popular, free scientific
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image processing application.</li>
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<li><a href="https://github.com/flika-org/flika">Flika</a> - An
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interactive image processing program for biologists written in
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Python.</li>
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<li><a href="https://github.com/Icy-imaging">Icy</a> - Open community
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platform for bioimage informatics, providing software resources to
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visualize, annotate and quantify bioimaging data.</li>
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<li><a href="https://github.com/ilastik/ilastik">Ilastik</a> - Simple,
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user-friendly tool for interactive image classification, segmentation
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and analysis.</li>
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<li><a href="https://github.com/imagej/ImageJ">ImageJ</a> - Public
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domain software for processing and analyzing scientific images.</li>
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<li><a href="https://github.com/imagej/imagej2">ImageJ2</a> - A Rewrite
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of ImageJ for multidimensional image data, with a focus on scientific
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imaging.</li>
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<li><a href="https://github.com/Image-Py/imagepy">ImagePy</a> - Open
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source image processing framework written in Python.</li>
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<li><a href="https://github.com/napari/napari">Napari</a> - Fast,
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interactive, multi-dimensional image viewer for Python.</li>
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<li><a href="https://github.com/opencv/opencv">OpenCV</a> - Open source
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computer vision and machine learning software library.</li>
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<li><a
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href="https://github.com/python-microscopy/python-microscopy">PYME</a> -
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Open-source application suite for light microscopy acquisition, data
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storage, visualization, and analysis.</li>
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<li><a
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href="https://github.com/scikit-image/scikit-image">Scikit-image</a> -
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Collection of algorithms for image processing.</li>
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</ul>
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<h2 id="image-processing-and-segmentation">Image processing and
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segmentation</h2>
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<ul>
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<li><a href="https://github.com/angelolab/ark-analysis">Ark-Analysis</a>
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- A pipeline toolbox for analyzing multiplexed imaging data.</li>
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<li><a href="https://github.com/pycroscopy/atomai">AtomAI</a> -
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PyTorch-based package for deep/machine learning analysis of microscopy
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data.</li>
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<li><a href="https://github.com/MouseLand/cellpose">Cellpose</a> - A
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generalist algorithm for cell and nucleus segmentation.</li>
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<li><a href="https://github.com/vanvalenlab/cellSAM">CellSAM</a> - A
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foundation model for cell segmentation trained on a diverse range of
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cells and data types.</li>
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<li><a href="https://github.com/Sentinal4D/cellshape">Cellshape</a> - 3D
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single-cell shape analysis of cancer cells using geometric deep
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learning.</li>
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<li><a href="https://clij.github.io/">CLIJ2</a> - GPU-accelerated image
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processing library for ImageJ/Fiji, Icy, MATLAB and Java.</li>
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<li><a href="https://github.com/vanvalenlab/deepcell-tf">DeepCell</a> -
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Deep learning library for single cell analysis.</li>
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<li><a href="https://github.com/BMIRDS/deepslide">DeepSlide</a> - A
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sliding window framework for classification of high resolution
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microscopy images.</li>
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<li><a href="https://github.com/aoles/EBImage">EBImage</a> - Image
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processing toolbox for R.</li>
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<li><a href="https://github.com/ziatdinovmax/GPim">GPim</a> - Gaussian
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processes and Bayesian optimization for images and hyperspectral
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data.</li>
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<li><a href="https://github.com/mahmoodlab/MAPS">MAPS</a> - MAPS
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(Machine learning for Analysis of Proteomics in Spatial biology) is a
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machine learning approach facilitating rapid and precise cell type
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identification with human-level accuracy from spatial proteomics
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data.</li>
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<li><a
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href="https://github.com/computational-cell-analytics/micro-sam">MicroSAM</a>
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- Tools for segmentation and tracking in microscopy build on top of
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SegmentAnything. Segment and track objects in microscopy images
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interactively.</li>
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<li><a href="https://github.com/ijpb/MorphoLibJ">MorpholibJ</a> -
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Collection of mathematical morphology methods and plugins for
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ImageJ.</li>
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<li><a href="https://github.com/aelefebv/nellie">Nellie</a> - Automated
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organelle segmentation, tracking, and hierarchical feature extraction in
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2D/3D live-cell microscopy.</li>
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<li><a href="https://github.com/4DNucleome/PartSeg">PartSeg</a> - A GUI
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and a library for segmentation algorithms.</li>
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<li><a href="https://github.com/dcjones/proseg">Proseg</a> : A cell
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segmentation method for in situ spatial transcriptomics.</li>
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<li><a href="https://github.com/Borda/pyImSegm">PyImSegm</a> - Image
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segmentation - general superpixel segmentation and center detection and
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region growing.</li>
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<li><a href="https://github.com/JackieZhai/SALEM2">Salem²</a> - Segment
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Anything in Light and Electron Microscopy via Membrane Guidance.</li>
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<li><a href="https://github.com/scverse/squidpy">Squidpy</a> - Python
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framework that brings together tools from omics and image analysis to
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enable scalable description of spatial molecular data, such as
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transcriptome or multivariate proteins.</li>
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<li><a href="https://github.com/stardist/stardist">StarDist</a> - Object
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detection with Star-convex shapes.</li>
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<li><a href="https://github.com/MouseLand/suite2p">Suite2p</a> -
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Pipeline for processing two-photon calcium imaging data.</li>
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<li><a href="https://github.com/georgeoshardo/SyMBac">SyMBac</a> -
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Accurate segmentation of bacterial microscope images using synthetically
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generated image data.</li>
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<li><a href="https://github.com/fiji/Trainable_Segmentation">Trainable
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Weka Segmentation</a> - Fiji plugin and library that combines a
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collection of machine learning algorithms with a set of selected image
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features to produce pixel-based segmentations.</li>
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</ul>
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<h2 id="ecology">Ecology</h2>
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<ul>
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<li><a href="http://ianzwchan.com/my-research/pat-geom/">PAT-GEOM</a> -
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A software package for the analysis of animal colour pattern.</li>
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<li><a href="https://github.com/gtatters/ThermImageJ">ThermImageJ</a> -
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ImageJ functions and macros for working with thermal image files.</li>
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</ul>
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<h2 id="neuroscience">Neuroscience</h2>
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<ul>
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<li><a href="https://github.com/axondeepseg/axondeepseg">AxonDeepSeg</a>
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- Segment axon and myelin from microscopy data using deep learning.</li>
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<li><a href="https://github.com/brainglobe/bg-atlasapi">BG-atlasAPI</a>
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- A lightweight Python module to interact with atlases for systems
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neuroscience.</li>
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<li><a href="https://github.com/brainglobe/brainreg">Brainreg</a> -
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Automated 3D brain registration with support for multiple species and
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atlases.</li>
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<li><a
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href="https://github.com/brainglobe/brainreg-napari">Brainreg-napari</a>
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- Automated 3D brain registration in napari with support for multiple
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species and atlases.</li>
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<li><a href="https://github.com/brainglobe/brainrender">Brainrender</a>
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- Python package for the visualization of three dimensional
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neuro-anatomical data.</li>
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<li><a href="https://github.com/flatironinstitute/CaImAn">CaImAn</a> -
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Computational toolbox for large scale Calcium Imaging Analysis.</li>
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<li><a href="https://github.com/brainglobe/cellfinder">Cellfinder</a> -
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Automated 3D cell detection and registration of whole-brain images.</li>
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<li><a
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href="https://github.com/brainglobe/cellfinder-napari">Cellfinder-napari</a>
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- Efficient cell detection in large images using <a
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href="https://brainglobe.info/cellfinder">cellfinder</a> in napari.</li>
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<li><a href="https://github.com/seung-lab/cloud-volume">CloudVolume</a>
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- Read and write Neuroglancer datasets programmatically.</li>
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<li><a href="https://github.com/natverse/nat">NeuroAnatomy Toolbox</a> -
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R package for the (3D) visualisation and analysis of biological image
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data, especially tracings of single neurons.</li>
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<li><a href="https://github.com/google/neuroglancer/">Neuroglancer</a> -
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WebGL-based viewer for volumetric data.</li>
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<li><a
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href="https://imagescience.org/meijering/software/neuronj/">NeuronJ</a>
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- An ImageJ plugin for neurite tracing and analysis.</li>
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<li><a href="https://www.nitrc.org/projects/panda/">Panda</a> - Pipeline
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for Analyzing braiN Diffusion imAges: A MATLAB toolbox for pipeline
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processing of diffusion MRI images.</li>
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<li><a href="https://github.com/zudi-lin/pytorch_connectomics">PyTorch
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Connectomics</a> - Deep learning framework for automatic and
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semi-automatic annotation of connectomics datasets, powered by
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PyTorch.</li>
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<li><a href="https://github.com/RivuletStudio/rivuletpy">RivuletPy</a> -
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Robust 3D Neuron Tracing / General 3D tree structure extraction in
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Python for 3D images powered by the Rivulet2 algorithm.</li>
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<li><a href="https://github.com/morphonets/SNT/">SNT</a> - ImageJ
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framework for semi-automated tracing and analysis of neurons.</li>
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<li><a href="https://github.com/AlbertPun/TRAILMAP/">TrailMap</a> -
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Software package to extract axonal data from cleared brains.</li>
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<li><a href="https://github.com/tractatus/wholebrain">Wholebrain</a> -
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Automated cell detection and registration of whole-brain images with
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plot of cell counts per region and Hemishpere.</li>
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<li><a
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href="https://github.com/ElisabethKugler/ZFVascularQuantification">ZVQ -
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Zebrafish Vascular Quantification</a> - Image analysis pipeline to
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perform 3D quantification of the total or regional zebrafish brain
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vasculature using the image analysis software Fiji.</li>
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</ul>
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<h2 id="plant-science">Plant science</h2>
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<ul>
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<li><a
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href="https://github.com/Gregor-Mendel-Institute/aradeepopsis">Aradeepopsis</a>
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- A versatile, fully open-source pipeline to extract phenotypic
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measurements from plant images.</li>
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<li><a
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href="https://github.com/Computational-Plant-Science/DIRT">DIRT</a> -
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Digital Imaging of Root Traits: Extract trait measurements from images
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of monocot and dicot roots.</li>
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<li><a href="https://zoegp.science/leafbyte">LeafByte</a> - Free and
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open source mobile app for measuring herbivory quickly and
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accurately.</li>
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<li><a
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href="https://mitobo.informatik.uni-halle.de/index.php/Applications/PaCeQuant">PaCeQuant</a>
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- An ImageJ-based tool which provides a fully automatic image analysis
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workflow for PC shape quantification.</li>
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<li><a
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href="https://github.com/jberry47/ddpsc_phenotypercv">PhenotyperCV</a> -
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Header-only C++11 library using OpenCV for high-throughput image-based
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plant phenotyping.</li>
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<li><a href="https://github.com/danforthcenter/plantcv">PlantCV</a> -
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Open-source image analysis software package targeted for plant
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phenotyping.</li>
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<li><a href="https://github.com/hci-unihd/plant-seg">PlantSeg</a> - Tool
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for cell instance aware segmentation in densely packed 3D volumetric
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images.</li>
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<li><a href="https://prbio-hub.github.io/rhizoTrak/">RhizoTrak</a> -
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Open source tool for flexible and efficient manual annotation of complex
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time-series minirhizotron images.</li>
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<li><a
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href="https://github.com/rootphenomicslab/RhizoVisionExplorer">Rhizovision
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Explorer</a> - Free and open-source software developed for estimating
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root traits from images acquired from a flatbed scanner or camera.</li>
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<li><a href="https://github.com/Abe404/root_painter">RootPainter</a> -
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Deep learning segmentation of biological images with corrective
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annotation.</li>
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</ul>
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<h2 id="fluoresence-in-situ-hybridization">Fluoresence in situ
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hybridization</h2>
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<ul>
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<li><a href="https://github.com/fish-quant/big-fish">Big-fish</a> -
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Python package for the analysis of smFISH images.</li>
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<li><a href="https://github.com/cbib/dypfish">DypFISH</a> - Python
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library for spatial analysis of smFISH images.</li>
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<li><a href="https://github.com/PreibischLab/RS-FISH">RS-FISH</a> - Fiji
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plugin to detect FISH spots in 2D/3D images which scales to very large
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images.</li>
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<li><a href="https://github.com/weigertlab/spotiflow">Spotiflow</a> - A
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deep learning-based, threshold-agnostic, and subpixel-accurate spot
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detection method developed for spatial transcriptomics workflows.</li>
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<li><a href="https://tissuumaps.github.io/">TissUUmaps</a> - Visualizer
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of NGS data, plot millions of points and interact, gate, export. ISS
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rounds and base visualization.</li>
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</ul>
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<h2 id="electron-and-super-resolution-microscopy">Electron and super
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resolution microscopy</h2>
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<ul>
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<li><a href="https://github.com/emx77/ASI_MTF">ASI_MTF</a> - ImageJ
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macro to calculate the modulation transfer function (MTF) based on a
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knife edge (or slanted edge) measurement.</li>
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<li><a href="https://github.com/TuragaLab/DECODE">DECODE</a> - Python
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and PyTorch based deep learning tool for single molecule localization
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microscopy.</li>
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<li><a href="https://github.com/volume-em/empanada">Empanada</a> -
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Panoptic segmentation algorithms for 2D and 3D electron microscopy
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images.</li>
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<li><a
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href="https://github.com/lukmuk/em-scalebartools">Em-scalebartools</a> -
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Fiji/ImageJ macros to quickly add a scale bar to an (electron
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microscopy) image.</li>
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<li><a href="https://github.com/jungmannlab/picasso">Picasso</a> - A
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collection of tools for painting super-resolution images.</li>
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<li><a href="https://github.com/jries/SMAP">SMAP</a> - A modular
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super-resolution microscopy analysis platform for SMLM data.</li>
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<li><a href="https://github.com/zitmen/thunderstorm">ThunderSTORM</a> -
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A comprehensive ImageJ plugin for SMLM data analysis and
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super-resolution imaging.</li>
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</ul>
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<h2 id="image-restoration-and-quality-assessment">Image restoration and
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quality assessment</h2>
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<ul>
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<li><a href="https://github.com/CSBDeep/CSBDeep">CSBDeep</a> - A deep
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learning toolbox for microscopy image restoration and analysis.</li>
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<li><a href="https://github.com/ij-plugins/ijp-color">Ijp-color</a> -
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Plugins for ImageJ - color space conversions and color calibration.</li>
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<li><a href="https://github.com/ocampor/image-quality">Image Quality</a>
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- Open source software library for Image Quality Assessment (IQA).</li>
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<li><a href="https://github.com/tlambert03/LLSpy">LLSpy</a> - Python
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library to facilitate lattice light sheet data processing.</li>
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<li><a href="https://github.com/HuanglabPurdue/NCS">NCS</a> - Noise
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correction algorithm for sCMOS cameras.</li>
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<li><a href="https://github.com/juglab/n2v">Noise2Void</a> - Learning
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denoising from single noisy images.</li>
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</ul>
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<h2 id="cell-migration-and-particle-tracking">Cell migration and
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particle tracking</h2>
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<ul>
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<li><a
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href="https://github.com/quantixed/CellMigration">CellMigration</a> -
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Analysis of 2D cell migration in Igor.</li>
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<li><a href="https://github.com/fiji/TrackMate">TrackMate</a> -
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User-friendly interface that allows for performing tracking, data
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visualization, editing results and track analysis in a convenient
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way.</li>
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<li><a href="https://github.com/quantixed/TrackMateR">TrackMateR</a> - R
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package to analyze cell migration and particle tracking experiments
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using outputs from TrackMate.</li>
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<li><a href="https://soft-matter.github.io/trackpy">Trackpy</a> - Fast
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and Flexible Particle-Tracking Toolkit.</li>
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<li><a href="https://gitlab.com/csb.ethz/tracx">TracX</a> - MATLAB
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generic toolbox for cell tracking from various microscopy image
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modalities such as Bright-field (BF), phase contrast (PhC) or
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fluorescence (FL) with an automated track quality assessment in absence
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of a ground truth.</li>
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<li><a
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href="https://imagej.net/plugins/trajclassifier">TraJClassifier</a> -
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Fiji plugin that loads trajectories from TrackMate, characterizes them
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using TraJ and classifiies them into normal diffusion, subdiffusion,
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confined diffusion and directed/active motion by a random forest
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approach (through Renjin).</li>
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<li><a href="https://github.com/CellDynamics/QuimP">QuimP</a> - Software
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for tracking cellular shape changes and dynamic distributions of
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fluorescent reporters at the cell membrane.</li>
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<li><a href="https://github.com/royerlab/ultrack">Ultrack</a> -
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Versatile cell tracking method for 2D, 3D, and multichannel timelapses,
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||
overcoming segmentation challenges in complex tissues.</li>
|
||
<li><a href="https://github.com/oist/usiigaci">Usiigaci</a> - Stain-free
|
||
cell tracking in phase contrast microscopy enabled by supervised machine
|
||
learning.</li>
|
||
</ul>
|
||
<h2 id="pathology">Pathology</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/AICAN-Research/FAST-Pathology">FastPathology</a>
|
||
- Open-source software for deep learning-based digital pathology.</li>
|
||
<li><a href="https://github.com/HistoCleanQUB/HistoClean">HistoClean</a>
|
||
- Tool for the preprocessing and augmentation of images used in deep
|
||
learning models.</li>
|
||
<li><a href="https://github.com/labsyspharm/minerva-story">Minerva</a> -
|
||
Image viewer designed specifically to make it easy for non-expert users
|
||
to interact with complex tissue images.</li>
|
||
<li><a href="http://www.orbit.bio">Orbit</a> - A versatile image
|
||
analysis software for biological image-based quantification using
|
||
machine learning, especially for whole slide imaging.</li>
|
||
<li><a href="https://github.com/Dana-Farber-AIOS/pathml">PathML</a> - An
|
||
open-source toolkit for computational pathology and machine
|
||
learning.</li>
|
||
<li><a
|
||
href="https://github.com/bayer-science-for-a-better-life/paquo">PAQUO</a>
|
||
- A library for interacting with QuPath from Python.</li>
|
||
<li><a href="https://qupath.github.io/">QuPath</a> - Open source
|
||
software for digital pathology image analysis.</li>
|
||
</ul>
|
||
<h2 id="mycology">Mycology</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/Olament/DeepMushroom">DeepMushroom</a> -
|
||
Image classification of fungus using ResNet.</li>
|
||
<li><a href="https://github.com/hsueh-lab/FFT">Fungal Feature Tracker
|
||
(FFT)</a> - Tool to quantitatively characterize morphology and growth of
|
||
filamentous fungi.</li>
|
||
</ul>
|
||
<h2 id="microbiology">Microbiology</h2>
|
||
<ul>
|
||
<li><a href="https://github.com/vrrenske/BactMAP">BactMap</a> - A
|
||
command-line based R package that allows researchers to transform cell
|
||
segmentation and spot detection data generated by different programs
|
||
into various plots.</li>
|
||
<li><a
|
||
href="https://drescherlab.org/data/bacstalk/docs/index.html">BacStalk</a>
|
||
- Interactive and user-friendly image analysis software tool to
|
||
investigate the cell biology of common used bacterial species.</li>
|
||
<li><a href="https://drescherlab.org/data/biofilmQ/docs/">BiofilmQ</a> -
|
||
Advanced biofilm analysis tool for quantifying the properties of cells
|
||
inside large 3-dimensional biofilm communities in space and time.</li>
|
||
</ul>
|
||
<h2 id="yeast-imaging">Yeast imaging</h2>
|
||
<ul>
|
||
<li><a href="https://git.ecdf.ed.ac.uk/swain-lab/baby/">BABY</a> - An
|
||
image processing pipeline for accurate single-cell growth estimation of
|
||
budding cells from bright-field stacks.</li>
|
||
<li><a href="https://github.com/rraadd88/htsimaging">htsimaging</a> -
|
||
Python package for high-throughput single-cell imaging analysis.</li>
|
||
<li><a href="https://yeastmate.readthedocs.io/en/latest/">YeastMate</a>
|
||
- Neural network-assisted segmentation of mating and budding events in
|
||
S. cerevisiae.</li>
|
||
<li><a href="https://github.com/lpbsscientist/YeaZ-GUI">YeaZ</a> - An
|
||
interactive tool for segmenting yeast cells using deep learning.</li>
|
||
</ul>
|
||
<h2 id="other">Other</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/AllenCellModeling/aicsimageio">AICSImageIO</a>
|
||
- Image reading, metadata conversion, and image writing for nicroscopy
|
||
images in Python.</li>
|
||
<li><a href="https://maweigert.github.io/biobeam">Biobeam</a> - 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.</li>
|
||
<li><a href="https://github.com/bonej-org/BoneJ2">BoneJ</a> - Collection
|
||
of Fiji/ImageJ plug-ins for skeletal biology.</li>
|
||
<li><a href="https://github.com/CBICA/CaPTk">CaPTk</a> - Cancer Imaging
|
||
Phenomics Toolkit: A software platform to perform image analysis and
|
||
predictive modeling tasks.</li>
|
||
<li><a href="https://github.com/Jhsmit/ColiCoords">ColiCoords</a> -
|
||
Python project for analysis of fluorescence microscopy data from rodlike
|
||
cells.</li>
|
||
<li><a
|
||
href="https://github.com/davidbhr/CompactionAnalyzer">CompactionAnalyzer</a>
|
||
- 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.</li>
|
||
<li><a
|
||
href="https://github.com/cytomining/cytominer-database">Cytominer-database</a>
|
||
- Command-line tools for organizing measurements extracted from
|
||
images.</li>
|
||
<li><a href="https://github.com/gcharvin/DetecDiv">DetecDiv</a> -
|
||
Comprehensive set of tools to analyze time microscopy images using deep
|
||
learning methods.</li>
|
||
<li><a href="https://github.com/mianalysis/mia">MIA</a> - Fiji plugin
|
||
which provides a modular framework for assembling image and object
|
||
analysis workflows.</li>
|
||
<li><a href="https://morphographx.org">MorphoGraphX</a> - Open source
|
||
application for the visualization and analysis of 4D biological
|
||
datasets.</li>
|
||
<li><a
|
||
href="https://github.com/AllenCellModeling/napari-aicsimageio">Napari-aicsimageio</a>
|
||
- Multiple file format reading directly into napari using pure
|
||
Python.</li>
|
||
<li><a href="https://github.com/05dirnbe/nefi">NEFI2</a> - Python tool
|
||
created to extract networks from images.</li>
|
||
<li><a href="https://github.com/adalca/neurite">Neurite</a> - Neural
|
||
networks toolbox focused on medical image analysis.</li>
|
||
<li><a
|
||
href="https://github.com/Open-Science-Tools/nd2reader">Nd2reader</a> - A
|
||
pure-Python package that reads images produced by NIS Elements
|
||
4.0+.</li>
|
||
<li><a href="https://github.com/zeiss-microscopy/OAD">OAD</a> -
|
||
Collection of tools and scripts useful to automate microscopy workflows
|
||
in ZEN Blue using Python and Open Application Development tools.</li>
|
||
<li><a href="https://github.com/cytomining/pycytominer">Pycytominer</a>
|
||
- Data processing functions for profiling perturbations.</li>
|
||
<li><a href="https://github.com/david-hoffman/pyotf">Pyotf</a> - A
|
||
simulation software package for modelling optical transfer functions
|
||
(OTF)/point spread functions (PSF) of optical microscopes written in
|
||
Python.</li>
|
||
<li><a
|
||
href="https://bitbucket.org/vladgaal/pyscratch_public.git/src">PyScratch</a>
|
||
- Open source tool that autonomously performs quantitative analysis of
|
||
in vitro scratch assays.</li>
|
||
<li><a href="https://github.com/rshkarin/quanfima">Quanfima</a> -
|
||
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.</li>
|
||
<li><a
|
||
href="https://github.com/SuperElastix/SimpleElastix">SimpleElastix</a> -
|
||
Multi-lingual medical image registration library.</li>
|
||
<li><a
|
||
href="https://alleninstitute.org/what-we-do/brain-science/research/products-tools/vaa3d/">Vaa3D</a>
|
||
- Open-source software for 3D/4D/5D image visualization and
|
||
analysis.</li>
|
||
<li><a href="https://github.com/spatialsimulator/XitoSBML">XitoSBML</a>
|
||
- ImageJ plugin which creates a Spatial SBML model from segmented
|
||
images.</li>
|
||
<li><a href="https://github.com/ekatrukha/ZstackDepthColorCode">Z-stack
|
||
Depth Color Code</a> - ImageJ/Fiji plugin to colorcode
|
||
Z-stacks/hyperstacks.</li>
|
||
<li><a
|
||
href="https://github.com/HenriquesLab/ZeroCostDL4Mic">ZeroCostDL4Mic</a>
|
||
- Google Colab to develop a free and open-source toolbox for
|
||
deep-Learning in microscopy.</li>
|
||
<li><a
|
||
href="https://github.com/lens-biophotonics/ZetaStitcher">ZetaStitcher</a>
|
||
- Tool designed to stitch large volumetric images such as those produced
|
||
by light-sheet fluorescence microscopes.</li>
|
||
</ul>
|
||
<h2 id="publications">Publications</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://febs.onlinelibrary.wiley.com/doi/10.1002/1873-3468.14451">A
|
||
Hitchhiker’s guide through the bio-image analysis software universe</a>
|
||
- An article presenting a curated guide and glossary of bio-image
|
||
analysis terms and tools.</li>
|
||
<li><a href="https://dx.doi.org/10.1038%2Fnmeth.2084">Biological imaging
|
||
software tools</a> - The steps of biological image analysis and the
|
||
appropriate tools for each step.</li>
|
||
<li><a href="https://doi.org/10.1038/nmeth.4397">Data-analysis
|
||
strategies for image-based cell profiling</a> - In-detail explanations
|
||
of image analysis pipelines.</li>
|
||
<li><a
|
||
href="https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.22909">Large-scale
|
||
image-based screening and profiling of cellular phenotypes</a> - A
|
||
workflow for phenotype extraction from high throughput imaging
|
||
experiments.</li>
|
||
<li><a
|
||
href="https://linkinghub.elsevier.com/retrieve/pii/S2472555222075943">Workflow
|
||
and metrics for image quality control in large-scale high-content
|
||
screens</a> - Approaches for quality control in high-content imaging
|
||
screens.</li>
|
||
</ul>
|
||
<h2 id="footnotes">Footnotes</h2>
|
||
<h3 id="similar-lists-and-repositories">Similar lists and
|
||
repositories</h3>
|
||
<ul>
|
||
<li><a href="https://biii.eu">BIII</a> - Repository of bioimage analysis
|
||
tools.</li>
|
||
<li><a
|
||
href="https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html">Bio-image
|
||
Analysis Notebooks</a> - Notebooks for bioimage analysis in Python.</li>
|
||
<li><a href="https://www.bioimagingguide.org">Bioimaging Guide</a> -
|
||
Microscopy for beginners reference guide.</li>
|
||
<li><a href="https://github.com/cytodata/awesome-cytodata">Cytodata</a>
|
||
- A curated list of awesome cytodata resources.</li>
|
||
<li><a href="https://www.napari-hub.org">Napari hub</a> - Collection of
|
||
napari plugins.</li>
|
||
<li><a
|
||
href="https://github.com/HohlbeinLab/OpenMicroscopy">OpenMicroscopy</a>
|
||
- Non-comprehensive list of projects and resources related to open
|
||
microscopy.</li>
|
||
</ul>
|
||
<p><a
|
||
href="https://github.com/hallvaaw/awesome-biological-image-analysis">biologicalimageanalysis.md
|
||
Github</a></p>
|