General image analysis software
13 projects
3D SlicerA free, open-source multi-platform software for 3D visualization and medical image analysis.
CellProfilerAn open-source application for automated biological image analysis, enabling biologists to measure phenotypes from thousands of images.
CellProfiler AnalystInteractive exploration and analysis software for large, high-dimensional image-derived biological data with supervised machine learning.
FijiA batteries-included distribution of ImageJ for scientific image processing, focused on life sciences research.
ImageJPublic domain Java software for processing and analyzing scientific images across multiple platforms.
ImageJ2An open-source, N-dimensional image processing platform for scientific imaging with a modular, headless architecture.
ImagePyAn open-source, plugin-based image processing framework in Python that integrates with numpy-based libraries like scikit-image and OpenCV.
napariA fast, interactive, multi-dimensional image viewer for Python designed for browsing, annotating, and analyzing large scientific images.
OpenCVAn open-source library with over 2500 optimized algorithms for real-time computer vision and machine learning.
PYMEAn open-source Python suite for light microscopy acquisition, storage, visualization, and analysis, with strong support for single-molecule localization techniques.
Scikit-imageA comprehensive library for image processing in Python with algorithms for segmentation, filtering, morphology, and feature detection.
Processing and segmentation
22 projects
Ark-AnalysisA Python toolbox for analyzing multiplexed imaging data, featuring segmentation, pixel/cell clustering, and spatial analysis.
AtomAIA PyTorch-based Python package for deep and machine learning analysis of microscopy data, designed for domain scientists.
CellposeA generalist algorithm for cellular segmentation with human-in-the-loop training and superhuman generalization across diverse microscopy images.
CellSAMA foundation model for cell segmentation that achieves state-of-the-art performance across diverse cellular targets and imaging modalities.
CellVit++A vision transformer-based deep learning model for automated instance segmentation and classification of cell nuclei in histopathology images.
DeepCellA deep learning library for single-cell analysis of biological images, specializing in cell segmentation and tracking.
DeepSlideA sliding window framework for classifying high-resolution whole-slide microscopy and histopathology images using deep neural networks.
EBImageAn R package for image processing and analysis with a focus on microscopy and biological imaging.
HoVer-NetA PyTorch-based deep learning model for simultaneous nuclear instance segmentation and classification in histopathology images.
MAPSA machine learning approach for rapid, pathologist-level cell type annotation from spatial proteomics data like MIBI and CODEX.
MicroSAMInteractive segmentation and tracking tools for microscopy images built on Segment Anything.
MorpholibJA library of mathematical morphology methods and plugins for ImageJ, extending its capabilities for 2D/3D image analysis.
NellieAn automated pipeline for organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy.
ProsegA probabilistic cell segmentation method for spatial transcriptomics data from platforms like Xenium, CosMx, MERSCOPE, and Visium HD.
PyImSegmA Python toolbox for image segmentation featuring superpixel segmentation, object center detection, and region growing with shape priors.
SquidpyA scalable Python toolkit for analyzing and visualizing spatial molecular data from tissue sections.
SplineDistA machine learning framework for automated cell segmentation in bioimages using parametric spline curves.
StarDistA Python library for 2D/3D object detection and instance segmentation in microscopy images using star-convex shapes.
Suite2pA complete pipeline for processing two-photon calcium imaging data, including registration, ROI detection, signal extraction, and spike deconvolution.
Trainable Weka SegmentationA Fiji plugin for pixel-based image segmentation using Weka machine learning algorithms and image features.
Ecology
1 projectsNeuroscience
14 projects
AxonDeepSegA deep learning tool for automatic axon and myelin segmentation from microscopy images using convolutional neural networks.
BG-atlasAPIA Python API for downloading and processing neuroanatomical atlas data from multiple sources.
BrainregAutomated 3D brain image registration tool for aligning sample data with anatomical atlases across multiple species.
BrainrenderA Python library for creating high-quality 3D visualizations of neuroanatomical data in atlas space.
CaImAnA Python toolbox for large-scale calcium and voltage imaging data analysis, including motion correction, source extraction, and spike deconvolution.
CellfinderAutomated 3D cell detection and classification in large-scale volumetric brain images using deep learning.
CloudVolumeA Python library for serverless, random-access reading and writing of Neuroglancer Precomputed format volumes, meshes, and skeletons.
NeuroAnatomy ToolboxAn R package for 3D visualization and analysis of biological image data, especially single neuron tracings.
NeuroglancerA WebGL-based viewer for visualizing volumetric data, 3D meshes, and skeletons in arbitrary cross-sectional views.
PyTorch ConnectomicsA PyTorch-based segmentation toolbox for electron microscopy connectomics, enabling neural structure analysis in 3D volumes.
SNTA complete framework for neuronal morphometry, from tracing and reconstruction to analysis, visualization, and modeling.
WholebrainR package for segmentation, registration, and web-based atlas generation from microscope brain images.
Plant science
6 projects
PlantCVAn open-source image analysis software package for plant phenotyping using computer vision.
PlantSegA tool for cell instance aware segmentation in densely packed 3D volumetric images, originally developed for plant tissues.
RootPainterA GUI-based tool for training deep neural networks to segment biological images using corrective annotation.