napari Plugin Accelerator Grants
These projects will drive development of napari’s growing ecosystem of plugins to provide easy access to reproducible and quantitative bioimage analysis, as well as support the maintenance of plugins. Read our Medium post.
Showing 41 results
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 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.
Anna Kreshuk Florian Jug
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 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 team will make multiple cutting-edge, content-aware denoising methods available in napari.
Christopher Schmied Joran Deschamps
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 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 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.
Christopher Schmied Florian Jug
This team will develop and release a napari plugin for use with the yt package to enable accessing multi-resolution data.
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 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 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 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 team will leverage napari for real-time microscope control with ImSwitch and develop plugins for microscopy users and builders.
Xavier Casas Moreno
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.
Pradeep Rajasekhar Nina Tubau Ribera
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.
Patrick La Riviere
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.
Raf Van de Plas
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 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 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.
Allyson Quinn Ryan
Carl D. Modes Robert Haase
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.
Ilaria Testa Carolina Wählby
This project will translate a collection of existing algorithms into a unified visualization and analysis plugin tailored for biological spectral microscopy.
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.