napari plugin to interactively train and test a StarDist model

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

This plugin provides tools for annotating spots in a 3D two-channel image (hdf5 type input file), submitting tiles for StarDist model generation or model re-training, and refining initial annotations based on predictions.

The objects of interest in the image are sphere-like spots with a diameter of just a few pixels and are thus well suited for StarDist instance segmentation. The image imensions are typically 1024x1024 pixels in xy and more than 100 sections in z.

With python and pip installed (e.g., via miniconda or miniforge), it is recommended to create a new environment and install napari-spofi using pip.

pip install napari napari-spofi

Start napari and select "spot finder (napari-spofi)" from the "plugin" menu.

In the 'annotation' widget, create a new directory for annotations. Add an image folder containing at least one h5 file. Select an image file, foreground and background channels. Load the image file.

Inspect the image for distinct regions. To help locate relevant tile positions, make the 'checkerboard' layer visible. While the 'tiles' layer is active, double-click a tile to add it to the list of tiles. This list will be used to generate a set of smaller images and masks for training purposes.

Switch to napari's 2D view. Navigate to the central section of individual spots and add points using the 'true' points layer. The heuristic built-in will annotate pixels that belong to individual spots. Some image enhancement step may be beneficial.

Annotate tiles in one or a few images. To prepare training data, use the 'extract spots' button.

Contributions are very welcome.

Distributed under the terms of the BSD-3 license, "napari-spofi" is free and open source software

If you encounter any problems, please [file an issue] along with a detailed description.