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 Awesome Biological Image Analysis !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
 Awesome Biological Image Analysis !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
 
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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.
- Scikit-image (https://github.com/scikit-image/scikit-image) - Collection of algorithms for image processing.
Image processing and segmentation
- Ark-Analysis (https://github.com/angelolab/ark-analysis) - A pipeline toolbox for analyzing multiplexed imaging data.
- AtomAI (https://github.com/pycroscopy/atomai) - PyTorch-based package for deep/machine learning analysis of microscopy data.
- Cellpose (https://github.com/MouseLand/cellpose) - A generalist algorithm for cell and nucleus segmentation.
- CellSAM (https://github.com/vanvalenlab/cellSAM) - A foundation model for cell segmentation trained on a diverse range of cells and data types.
- Cellshape (https://github.com/Sentinal4D/cellshape) - 3D single-cell shape analysis of cancer cells using geometric deep learning.
- CLIJ2 (https://clij.github.io/) - GPU-accelerated image processing library for ImageJ/Fiji, Icy, MATLAB and Java.
- DeepCell (https://github.com/vanvalenlab/deepcell-tf) - Deep learning library for single cell analysis.
- DeepSlide (https://github.com/BMIRDS/deepslide) - A sliding window framework for classification of high resolution microscopy images.
- EBImage (https://github.com/aoles/EBImage) - Image processing toolbox for R.
- GPim (https://github.com/ziatdinovmax/GPim) - Gaussian processes and Bayesian optimization for images and hyperspectral data.
- 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.
- 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.
- 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.
- MorpholibJ (https://github.com/ijpb/MorphoLibJ) - Collection of mathematical morphology methods and plugins for ImageJ.
- Nellie (https://github.com/aelefebv/nellie) - Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy.
- PartSeg (https://github.com/4DNucleome/PartSeg) - A GUI and a library for segmentation algorithms.
- Proseg (https://github.com/dcjones/proseg) : A cell segmentation method for in situ spatial transcriptomics.
- PyImSegm (https://github.com/Borda/pyImSegm) - Image segmentation - general superpixel segmentation and center detection and region growing.
- Salem² (https://github.com/JackieZhai/SALEM2) - Segment Anything in Light and Electron Microscopy via Membrane Guidance.
- 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.
- StarDist (https://github.com/stardist/stardist) - Object detection with Star-convex shapes.
- Suite2p (https://github.com/MouseLand/suite2p) - Pipeline for processing two-photon calcium imaging data.
- SyMBac (https://github.com/georgeoshardo/SyMBac) - Accurate segmentation of bacterial microscope images using synthetically generated image data.
- 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.
- 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.
Ecology
- PAT-GEOM (http://ianzwchan.com/my-research/pat-geom/) - A software package for the analysis of animal colour pattern.
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- SNT (https://github.com/morphonets/SNT/) - ImageJ framework for semi-automated tracing and analysis of neurons.
- TrailMap (https://github.com/AlbertPun/TRAILMAP/) - Software package to extract axonal data from cleared brains.
- Wholebrain (https://github.com/tractatus/wholebrain) - Automated cell detection and registration of whole-brain images with plot of cell counts per region and Hemishpere.
- 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.
- 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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- CellMigration (https://github.com/quantixed/CellMigration) - Analysis of 2D cell migration in Igor.
- 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.
- TrackMateR (https://github.com/quantixed/TrackMateR) - R package to analyze cell migration and particle tracking experiments using outputs from TrackMate.
- 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
- Trackpy (https://soft-matter.github.io/trackpy) - Fast and Flexible Particle-Tracking Toolkit.
- 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
absence of a ground truth.
- 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).
- 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).
- QuimP (https://github.com/CellDynamics/QuimP) - Software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane.
- Ultrack (https://github.com/royerlab/ultrack) - Versatile cell tracking method for 2D, 3D, and multichannel timelapses, overcoming segmentation challenges in complex tissues.
- Usiigaci (https://github.com/oist/usiigaci) - Stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning.
Pathology
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Yeast imaging
- BABY (https://git.ecdf.ed.ac.uk/swain-lab/baby/) - An image processing pipeline for accurate single-cell growth estimation of
budding cells from bright-field stacks.
- htsimaging (https://github.com/rraadd88/htsimaging) - Python package for high-throughput single-cell imaging analysis.
- YeastMate (https://yeastmate.readthedocs.io/en/latest/) - Neural network-assisted segmentation of mating and budding events in S. cerevisiae.
- YeaZ (https://github.com/lpbsscientist/YeaZ-GUI) - An interactive tool for segmenting yeast cells using deep learning.
Other
- AICSImageIO (https://github.com/AllenCellModeling/aicsimageio) - Image reading, metadata conversion, and image writing for nicroscopy images in Python.
- 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.
- BoneJ (https://github.com/bonej-org/BoneJ2) - Collection of Fiji/ImageJ plug-ins for skeletal biology.
- CaPTk (https://github.com/CBICA/CaPTk) - Cancer Imaging Phenomics Toolkit: A software platform to perform image analysis and predictive modeling tasks.
- ColiCoords (https://github.com/Jhsmit/ColiCoords) - Python project for analysis of fluorescence microscopy data from rodlike cells.
- 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
- 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.
- Cytominer-database (https://github.com/cytomining/cytominer-database) - Command-line tools for organizing measurements extracted from images.
- DetecDiv (https://github.com/gcharvin/DetecDiv) - Comprehensive set of tools to analyze time microscopy images using deep learning methods.
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- Cytodata (https://github.com/cytodata/awesome-cytodata) - A curated list of awesome cytodata resources.
- Napari hub (https://www.napari-hub.org) - Collection of napari plugins.
- OpenMicroscopy (https://github.com/HohlbeinLab/OpenMicroscopy) - Non-comprehensive list of projects and resources related to open microscopy.
biologicalimageanalysis Github: https://github.com/hallvaaw/awesome-biological-image-analysis