WIPP - Web Image Processing Pipeline

Web Image Processing Pipeline (WIPP) has been designed for enabling interactive measurements and discoveries over very large images. For more information, please, visit the Activity web page.

For any questions or issues, please send an email to the WIPP team.

NIST article and video: A Map App to Track Stem Cells, NIST news, February 13, 2018

Reference book: Web Microanalysis of Big Image Data , published in January 2018 by Springer Verlag.

WIPP 3.xx includes support for adding algorithmic plugins by leveraging containerization and execution orchestration technologies, such as Docker and Kubernetes. The beta WIPP client-server system consists of multiple components including WIPP REST API and UI, MongoDB, Argo Workflows, JupyterLab, Plots, and Tensorboard.

WIPP deployment instructions (beta version - 3.0.0)

We recommend WIPP deployment using Docker containers. The deployment consists of installing Docker engine, Docker swarm, and four Docker images (MongoDB, WIPP, EXEC, and Master).

WIPP deployment instructions (current version - 2.3.0)
WIPP deployment instructions (release 2.2.1)
WIPP deployment instructions (release 2.2.0)
WIPP deployment instructions (release 2.1.0)
WIPP deployment instructions (release 2.0.0)
WIPP deployment instructions (release 1.1.0)
WIPP deployment instructions (release 1.0.0)

WIPP User Guide (current version - 2.3.0)
WIPP User Guide (release 2.2.1)
WIPP User Guide (release 2.2.0)
WIPP User Guide (release 2.1.0)
WIPP User Guide (release 2.0.0)
WIPP User Guide (release 1.1.0)
WIPP User Guide (release 1.0.0)

A complete set of test data for testing WIPP image processing and feature extraction jobs is provided with detailed instructions:
Download WIPP test datasets

Additional datasets are available:
5x5 Image Tile Dataset
Cy5 Test Images (~54MB)
Phase Test Images (~83MB)

10x10 Image Tile Dataset
Cy5 Test Images (~119MB)
Phase Test Images (~195MB)

Use Case 1: Cell Count and Single Cell Detection
Use Case 2: Stem Cell Colony Growth Computation
Use Case 3: Image Feature Variability and Its Impact