Cellpose is a deep-learning (DL) based algorithm for cell and nucleus segmentation. It was created by the Stringer and Pachitariu groups and was originally published in the Stringer et al., Nature Methods, 2021.

Cellpose uses a cell detection method that predicts object shape using the flow-representation of object cell dynamics that is well-suited to approximate and define the complex borders of the cells in the microscopy images. These representations are provided for the DL model training and predictions (inference). Full documentation of the method can be found on the cellpose website.

 

Vision4D can be configured to execute cellpose segmentation within analysis pipelines, thereby enabling users to take advantage of both the advanced segmentation enabled by cellpose and the image and segment processing and visualization tools offered by Vision4D. This article explains how to download and install the necessary tools, and how to configure the pipeline Python Segmenter operation to segment objects using cellpose. 

By integrating cellpose into a pipeline, users can take advantage of the full functionality of the Vision4D pipeline concept to:

• Process large multidimensional images
• Enable segmentation in an easy-to-use interface
• Enable the visualization of objects segmented using cellpose in 4D with advanced object display options
• Facilitate complex further analysis like parent-child relationships and tracking