This article explains how to integrate cellpose into an arivis Vision4D pipeline.
Applying cellpose models (arivis Vision4D 3.4.0 to 4.1.0)
Introduction
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
Preliminary Remarks
Vision4D runs deep learning applications for instance segmentation such as Cellpose and StarDist using external and arivis-independent Python libraries and tools produced by third parties.
These tools must be installed by the user under their own responsibility, strictly following the instructions in this document. arivis has tested the setup protocol on several computers, however, due to the different and unpredictable hardware and software configurations of any given computer system, the results may vary on a case-by-case basis. Therefore, arivis declines any responsibility concerning the correct tools, installation, and setup on the individual user’s workstation. arivis cannot be made responsible for any malfunctioning or failure of the deep learning environment setup. arivis does not guarantee technical support on the setup task or on any deep learning application. Furthermore, arivis also declines any responsibility regarding the validity of the scientific results gathered from the deep learning application.
Installing the prerequisites
To use the cellpose script in Vision4D, we need 3 configuration steps:
- Install Anaconda Python
- Import the cellpose environment
- Configure the Vision4D Machine Learning preferences to use the environment.
Installing Anaconda
Instructions for downloading, installing, and configuring anaconda for Vision4D can be found here.