update lists
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@@ -47,11 +47,11 @@ Biological image analysis aims to increase our understanding of biology through
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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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@@ -61,8 +61,11 @@ Biological image analysis aims to increase our understanding of biology through
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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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@@ -143,10 +146,12 @@ Biological image analysis aims to increase our understanding of biology through
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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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@@ -170,11 +175,11 @@ absence of a ground truth.
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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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@@ -221,3 +226,6 @@ budding cells from bright-field stacks.
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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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