napari Ecosystem Grants
These grants support the napari project and its growing ecosystem of plugins. Read our Medium post.
Showing 77 results
This project will extend the capabilities and increase the users of structured illumination microscopy (SIM).
This project will develop a napari plugin for spot finding, filtering, and localization using a physics and probability-driven approach.
This project will provide support for more image types and documentation and demos of how to use it for various image types and segmentation targets.
This project will develop a plugin that can create a variety of annotations needed for machine learning, including deep learning, on a variety of biological images.
This project will provide software for 2+ dimensional image annotation, which enhances the speed of labeling using object selection and semi-automatic methods with both simple and more enhanced algorithms.
This project will develop a napari plugin for wsireg, a software for whole slide image registration of 2D multimodal image sets and sequential 2D images for 3D reconstruction.
This project will provide an interactive napari interface for metabolite and lipid annotations of mass spectrometry imaging data.
This project will improve usability of quantitative biomechanical stress measurements in 3D data in napari.
This project will enhance the napari-BIL-Data-Viewer plugin to support all compatible whole brain datasets within the Brain Image Library national archive, along with associated neuron tracings.
This project will support napari as an interactive environment and strengthen the connections between plugins and the wider napari ecosystem through tutorials and plugin improvements.
This team will build plugin infrastructure for simplifying the development of AI-powered plugins in napari, connect to the BioImage Model Zoo, and enable sharing of pretrained AI models across plugins and tools.
This project will implement the popular ImageJ/Fiji image registration plugins StackReg/TurboReg in napari.
This team will develop a new plugin to bring the entire image analysis functionality of the BrainGlobe computational neuroanatomy suite to napari.
This project will create a napari plugin based on skan, a Python library for skeleton analysis.
This project will refactor the ImageJ NanoJ plugin framework, including SRRF, into an ecosystem of napari plugins to enable next-generation, high-performance, super-resolution microscopy analysis.
This project will translate the existing software AIDeveloper into a plugin for napari.
This team will develop napari plugins for widespread training and use of deep learning models for restoring and segmenting microscope images.
This project will provide user-friendly access to deep learning-based bright field image segmentation in napari.
This project will make image segmentation more accessible by developing an easy to train pixel-classifier for the napari community and improving its documentation for use by a wide audience.
This project will enhance and document the arcos-gui plugin for automatic recognition of collective signaling in time-lapse images of 2D and 3D cell communities.
This team will make multiple cutting-edge, content-aware denoising methods available in napari.
This team will translate the ImageJ script MethodsJ2 to Python and integrate it into napari so that the microscope hardware and software metadata can be collected and used to automatically generate manuscript methods text.
This project will provide research engineering support to improve and develop single-cell tracking tools for the napari community.
This team will engage new users of the Allen Cell & Structure Segmenter napari plugin and provide user support, including plugin maintenance and development to increase utility and impact in the community.
This project will bring the functionality of Fiji’s popular Correct-3D-Drift macro to napari for flexible and efficient correction of stage and sample drift common in time-lapse microscopy.
This team will develop an interactive and user-friendly napari plugin for the segmentation of noisy images that minimizes the amount of training data generated manually.
This team will develop and release a napari plugin for use with the yt package to enable accessing multi-resolution data.
This project seeks to segment, track, and quantitatively analyze dynamic cell shape and packing of epithelial cells from 4D imaging data.
This project will develop a pipeline for quantifying whole-brain neural activity from volumetric video microscopy of C. elegans and other organisms, including an interactive app for visualization and curation.
This team will make state-of-the-art segmentation algorithms available to the whole bioimage analysis community as an interactive napari plugin.
This team will enable plugin developers to rapidly build new plotting widgets with the ability to trace back individual data points to their original object within an image stack.
This team will provide a convenient tool with deep learning assistance for the quick and easy creation of annotated data of arbitrary size as the adaptation of the original ImageJ plugin.
This project will develop single-particle tracking and analysis packages that allow researchers to infer biophysical properties of cells from the motion of fluorescent nanoparticles.
This project will segment 3D cells of irregular shapes by converting VollSeg jupyter notebooks into a napari plugin
This team will maintain napari-imc, a napari plugin for imaging mass cytometry file format support, and generalize it to other commonly used hierarchical file formats, such as HDF5 and zarr.
This project will bring human-in-the-loop cell tracking into the napari ecosystem to fulfill common cell tracking, editing, and analysis needs.
This project will improve the quality, reliability, and usability of the napari feature classifier plugin.
This project will increase the accessibility of napari-plot and expand it with documentation, tutorials, and example implementations, showing how napari-plot can be seamlessly integrated and used alongside 2D and 3D visualizations.
This project will review and improve the napari fundamental layer models, ensuring they are truly foundational for all plugin data.
This team will enable user-friendly access to general purpose GPU-accelerated bioimage analysis in Python and napari.
This project will make ImageJ/ImageJ2/Fiji functionality, including extensions, accessible from the napari user interface, and enhance napari as needed to facilitate such integration.
This project will maintain and improve the napari plugin affinder for manual registration, and plugin zarpaint for manual segmentation and proofreading of larger-than-RAM images.
This project will improve the functionality, documentation, and usability of the Napari Layer Table Plugin.
This team will convert the most widely used microscopy illumination correction tool, BaSiC, into a napari plugin pyBaSiC, including latest tool developments.
This project will implement SplineDist as a napari plugin, expanding the range of applications of the plugin StarDist to any object shape, while requiring fewer parameters.The goal of this project is to translate SplineDist into a napari plugin to increase access for users without coding expertise and provide an interactively editable visualization tool. The plugin, splinedist-napari, will bring value to the imaging community because it complements the already highly valuable stardist-napari by extending its usability to non-star-convex shapes.
This project will turn the napari-matplotlib prototype into a stable plugin by setting up infrastructure and tooling to facilitate contributions from the wider community.
This team will leverage napari for real-time microscope control with ImSwitch and develop plugins for microscopy users and builders.
This team will deliver seamless usability of cellfinder in napari, reduce barriers of entry and widen application to other areas of biology.
This project will facilitate the creation and use of vector graphics representations for bioimage annotation, segmentation, and shape analysis.
This project will develop an integrative visualization tool for spatial omics data to support interactive exploration of annotated gene-expression profiles and associated high-resolution images.
This project will build a napari plugin to support the interactive visualization of large-scale or streaming analysis of brain imaging data via the CaImAn open source package.
This team will develop a user-friendly napari plugin for data handling, visualization, and analysis of lattice light sheet microscopy data.
This team will adapt existing Python code for napari to acquire, process, and display light field images, and further enhance the code for processing light field images using deep learning approaches.
This team will develop and maintain the napari-1D plugin that broadens the scope of napari to provide support for 1D visualization and two-way interactive communication between 1D and 2D/3D environments.
This project will maintain, support and improve the NucleAIzer plugin developed during the napari Alfa Cohort.
This project will convert the stand-alone image analysis software MM3 for the widely used microfluidic continuous culture device to a napari plugin.
This project will develop the Phasor plot plugin for napari, which will perform phasor analysis on microscopy hyperspectral imaging (HSI), enabling cell biologists to quantify and segment cells and tissue according to their fluorescence spectrum at the single-pixel level.
This project will improve the functionality of napari-annotatorj, a plugin intended for 2D image annotation.
This project will develop a napari plugin capable of connecting cameras and feeding recorded images on the image viewer, allowing fast prototyping, real-time image processing, and workflow streamlining.
This project will develop a plugin that loads, visualizes and analyzes single-molecule localization data acquired by super-resolution microscopy in biomedical research.
This project will upgrade napari-micromanager, a plugin that brings together the high-performance image visualization and analysis of napari with Python-based control of microscopes based on the Micro-Manager C++ core.
This project will provide a napari plugin for cell biologists to study cellular dynamics using microscopy both on a morphological level in terms of cell shape and on internal protein dynamics.
This project will develop a plugin for the interactive exploration of multiplexed imaging data displayed in napari with multi-omic and 3D genome structure models displayed in Nucleome Browser.
This project will maintain and extend the functionality of the StarDist object detection napari plugin developed during the napari Alfa Cohort.
This team will develop a napari plugin to annotate keypoint and segmentation data for DeepLabCut.
This project will develop napari plugins to enable single-cell tracking and track editing.
This team will develop a napari GUI plugin for interactive removal of fluorescence spillover and autofluorescence without reference spectra via a GPU-accelerated minimization of mutual information.
This project will transform the napari-psf-simulator into a comprehensive point-spread function (PSF) simulation tool for fluorescence microscopy, including widefield, light sheet, confocal, and super-resolution microscopy.
This team will translate a package of Python functions to napari and add new capability so that users can process and analyze polarised light microscopy datasets.
This team will develop a plugin for real-time microscope and fluidics control to turn any microscope into a sequencing machine for in situ sequencing.
This project will improve the napari-roi-registration plugin by adding support and allowing extraction of quantitative information, promoting time-lapse registration within the biological community.
This project will document existing image analysis napari plugins in streamlined workflows and improve interoperability.
This funding opportunity supports the long-term sustainability of the napari project by supporting community-building activities and matching funds from other donors.
This project will translate a collection of existing algorithms into a unified visualization and analysis plugin tailored for biological spectral microscopy.
This project will develop a napari super-resolution microscopy plugin to foster global access to fluorescence nanoscopy imaging.
This team will develop a plugin for constructing spatial object graphs (e.g., cell graphs) from segmented images, visualizing them as image/mask overlays in napari and performing topological network analysis.
This project will enhance vollseg-napari by providing documentation, trained models, and a model training module to integrate it with existing napari plugins for tracking, skeletonization and segmentation metrics.