Segmentation Filter Overlap


A simple plugin to remove overlapping segmentations from images

Workflow step:
Image annotation
Image segmentation

Binder Python 3.11 Commit activity GitHub

PARSEG (PAralellised Refinement of SEGmentations) combines segmentation masks and filters overlapping objects based on colocalization statistics, such as percent overlap.

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


By leveraging Dask, PARSEG filters overlapping segmentations masks in a computationally efficient manner by processing individual 2D slices in parallel.

There are two different ways to interact with PARSEG and use it for different objectives:

  • As a napari plugin for graphical user interaction
  • As a Python API to allow users to integrate PARSEG tools into their own custom workflows


You can install napari-segmentation-overlap-filter via pip:

pip install napari-segmentation-overlap-filter

Getting Started

Napari Plugin

  1. Download the example dataset images
  2. Start napari and open both images as separate layers
  3. Convert the layers from an Image Layer to a Labels Layer by right-clicking on the layer
  4. Open the plugin with Plugins > Segmentation Overlap Filter and the widget will appear on the right
  5. Select the two segmentation masks you'd like to combine using the drop down and menu
  6. Drag the slider to set percent overlap allowed
  7. Click Run
  8. Optionally, export the overlap dataframe as a csv file

Python API

This example notebook shows how you can integrate the Python API into your own workflow for filtering and combining overlapping segmentation masks


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


  • 0.0.1

Last updated:

  • 24 June 2024

First released:

  • 24 June 2024


Supported data:

  • Information not submitted

Plugin type:

GitHub activity:

  • Stars: 0
  • Forks: 0
  • Issues + PRs: 6

Python versions supported:

Operating system:


  • numpy
  • magicgui
  • qtpy
  • scikit-image