The napari hub is transitioning to a community-run implementation due to launch in June 2025.
Since October 1, 2024, this version is no longer actively maintained and will not be updated. New plugins and plugin updates will continue to be listed.

napari-spotiflow

napari-spotiflow

Napari plugin for Spotiflow

License: BSD-3 PyPI Python Version tests PyPI - Downloads napari hub

Logo

Napari plugin for Spotiflow, a deep learning-based, threshold-agnostic, and subpixel-accurate spot detection method for 2D and 3D fluorescence microscopy images. The plugin allows using several pre-trained models as well as user-trained ones. For the main repository, see here.

https://github.com/weigertlab/napari-spotiflow/assets/11042162/02940480-daa9-4a21-8cf5-ad73c26c9838

If you use this plugin for your research, please cite us.


Installation

The plugin can be installed directly from PyPi (make sure you use a conda environment with napari and spotiflow installed):

pip install napari-spotiflow

Usage

  1. Open the image (or open one of our samples, e.g. File > Open Sample > napari-spotiflow > HybISS)
  2. Start the plugin Plugins > napari-spotiflow
  3. Select model (pre-trained or custom trained) and optionally adjust any other parameters
  4. Click Run

Supported input formats

  • 2D (YX, YXC or CYX)
  • 2D+t (TYX, TYXC or TCYX)
  • 3D (ZYX, ZYXC or CZYX)
  • 3D+t (TZYX, TZYXC or TCZYX)

How to cite

See the main repository's How to cite section.

Version:

  • 0.3.4

Last updated:

  • 07 November 2024

First released:

  • 02 February 2024

License:

Supported data:

  • Information not submitted

Open extension:

GitHub activity:

  • Stars: 10
  • Forks: 0
  • Issues + PRs: 0

Python versions supported:

Operating system:

Requirements:

  • spotiflow
  • npe2
  • napari>=0.5