Bioimage Analysis
Showing 32 of 32 projects
A generalist algorithm for cellular segmentation with human-in-the-loop training and superhuman generalization across diverse microscopy images.
An open-source, plugin-based image processing framework in Python that integrates with numpy-based libraries like scikit-image and OpenCV.
A Python library for 2D/3D object detection and instance segmentation in microscopy images using star-convex shapes.
Interactive segmentation and tracking tools for microscopy images built on Segment Anything.
A vision-language foundation model for computational pathology, pretrained on 1.17M histopathology image-caption pairs for diverse AI tasks.
A deep learning library for single-cell analysis of biological images, specializing in cell segmentation and tracking.
Automated 3D cell detection and classification in large-scale volumetric brain images using deep learning.
A foundation model for cell segmentation that achieves state-of-the-art performance across diverse cellular targets and imaging modalities.
A scalable cell tracking method for 2D, 3D, and multichannel timelapse recordings, robust under segmentation uncertainty.
A semi-automated pipeline for instance-aware cell segmentation, tracking, and migration analysis in phase contrast microscopy using Mask R-CNN.
A curated list of software, tools, pipelines, and plugins for image analysis in biological research.
Interactive exploration and analysis software for large, high-dimensional image-derived biological data with supervised machine learning.
A deep learning-based, threshold-agnostic, subpixel-accurate 2D and 3D spot detection method for fluorescence microscopy and spatial transcriptomics.
A tool for cell instance aware segmentation in densely packed 3D volumetric images, originally developed for plant tissues.
A deep learning tool for automatic axon and myelin segmentation from microscopy images using convolutional neural networks.
A Fiji plugin for pixel-based image segmentation using Weka machine learning algorithms and image features.
A library of mathematical morphology methods and plugins for ImageJ, extending its capabilities for 2D/3D image analysis.
A Python toolbox for analyzing multiplexed imaging data, featuring segmentation, pixel/cell clustering, and spatial analysis.
An automated pipeline for organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy.
A curated list of software, datasets, and publications for image-based profiling of biological phenotypes in drug discovery and cell biology.
A curated list of software, datasets, and publications for image-based profiling of biological phenotypes in drug discovery and cell biology.
A machine learning framework for automated cell segmentation in bioimages using parametric spline curves.
A complete framework for neuronal morphometry, from tracing and reconstruction to analysis, visualization, and modeling.
An open-source tool for precise, interactive, fast, and scalable spot detection in 2D/3D microscopy images for FISH-based spatial genomics.
A 3D convolutional network software package for extracting axonal and filamentous structures from cleared brain imaging data.
Tool for segmentation in bioimaging
A foundation model-driven method for semantic cell classification in whole slide images, extending Cellpose with a WSI workflow and QuPath integration.
An automated Fiji-based image analysis pipeline for 3D quantification of zebrafish brain vasculature from light sheet fluorescence microscopy data.
A Python package to quantify fiber alignment and density around cells in 3D fiber matrices as a proxy for cellular contractile forces.
An ImageJ plugin that converts segmented images into Spatial SBML models for spatial biological simulations.
Segment Anything in Light and Electron Microscopy via membrane guidance for biomedical image analysis.
A tool for panoptic segmentation of organelles in 2D and 3D electron microscopy images using deep learning.
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