Skip to main content

Have you used our open science resources?

Allen Cell Segmenter | Tutorial

The goal of the Allen Cell Segmenter was to extract the most high-quality and biologically informative segmentations in 3d images.
Texture Background Image Texture Background Image

The Allen Cell and Structure Segmenter is a Python-based, open-source toolkit that allows users to combine classic image segmentation with iterative deep learning workflows. Users can input 3D image stacks and output binary images that can be used for analysis. The Segmenter can be accessed with the napari plugin (for those with limited coding knowledge) or through Jupyter Notebook and Python. Use cases include biologists who study human cells who need to segment nuclei in an image dataset to count cells, measure cell density, and nuclei volume in different experiment conditions; biologists who study mitochondria in yeast and need to segment mitochondria to estimate mitochondrial volume; and data analysts with limited biology knowledge but experience analyzing images who want to automate segmentation to quantify a variety of cell features in a large imaging dataset. To begin, first download and install the nepari app. 

 Allen Cell and Structure Segmenter

Napari plugin

Napari app


Science Programs at Allen Institute