Welcome to the PAIS and PIDB wiki!

Introduction

The systematic analysis of imaged pathology specimens results in a vast amount of morphological information at both the cellular and sub-cellular scales. The information generated by this process has tremendous potential for providing insight on the underlying mechanisms responsible for disease onset and progression. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of complex data resulting from the analysis and annotation of large digital pathology data sets.

We have developed a Pathology Analytical Pathology Imaging Standards (PAIS) data management system to model, manage and query analytical results and human annotations of pathology images, and a Pathology Image Database System (PIDB) to model, manage, and query whole slide images. The software has been deployed as the core data infrastructure for the NCI In Silico Brain Tumor Research Center at Emory University to support integrated morphologic analysis, managing 200 million nuclei and 15 billion image features. PAIS is deployed at the Cancer Institute of New Jersey for managing analytical results and annotations from 4740 tissue microarray discs (histospots) of breast cancer. In the algorithm validation project at Emory University, PAIS is used as the backend engine for algorithm evaluation. In the project of Informatics for Integrative Brain Tumor Whole Slide Analysis, the software is used as the data management infrastructure for analytical results derived from multiplex quantum dot IHC images.

The PAIS system includes three modules:

  • Database design and management
  • Data generation, pre-processing and uploading
  • Data analysis and visualization.

Detailed information about these modules is presented in the following.

Database Design and Management

We employ DB2 spatial database to store and manage our spatial data. To store the whole slide images and the related information, we propose two database schemas: pi and pais. To analyze the image data, we provide some user-defined functions and stored procedures.

You can find how to create database, schema and stored procedure here.

Data Generation, Pre-processing and Uploading