This article explains how to integrate cellpose into an arivis Vision4D pipeline.
Applying cellpose models (arivis Vision4D 3.4.0 to 4.1.0)
Overview
To use cellpose in Vision4D we need the following:
- Install anaconda python
- Import the cellpose environment in the Anaconda Navigator
- Configure Vision4D to install the arivis libraries into the cellpose environment
- Use the Python Segmenter operation to load the cellpose script and define the required parameters
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.
Using cellpose in Vision4D
Using cellpose in pipelines
Cellpose is a powerful segmentation tool for bioimage analysis, and it is free to use and does not require commercial software like Vision4D. However, there are advantages to using cellpose within Vision4D pipelines.
The first main advantage of using cellpose in Vision4D was alluded to above. This implementation allows users to use the method with images of virtually any size on virtually any computer that runs MS Windows. It can therefore be used to segment very large 2D and 3D datasets like slide scans and Light-Sheet scans.
The second advantage of using cellpose in Vision4D is the ability to use it in conjunction with other pipeline operations to refine the results, extract additional information, and enable easy-to-use visualization tools to both review and present the results of analyses.
Building complex pipelines that use cellpose, users can:
- Use pre-segmentation enhancements to create more easily segmented images
- Refine the results of the segmentation to remove segmentation artifacts like small/large objects that shouldn't be segmented,
- Use cellpose segmentation results together with traditional segmentation tools to identify inter-object relationships like finding children objects inside cells or distances to neighbors
Many of these possibilities are covered in the User Guide that users can access from the help menu, and in other articles on this website. Please use the search tool to find out more about compartments analysis, object coloring options, movie making, etc.
Download Full "How to: install and run predictions with Cellpose" PDF