# Presentation: 620238

## caBIG Technologies In Practice

- Grid-Enabling
- Scientific and Clinical Applications

## Applications

**CVRG aECG analysis workflow**
- gridIMAGE Radiology Central Review
- Microscopy Analysis and caMicroscope

## Cardiovascular Research Grid (CVRG)

- Funded by the National Heart, Lung and Blood Institute to facilitate collaboration between cardiac researchers around the world
- Joint effort
- Institute for Computational Medicine, Johns Hopkins
- Department of Biomedical Informatics, The Ohio State University Medical Center
- Center for Research in Biological Systems, University of California, San Diego
- Research Aims
- Determine normal and disease states for all data types through statistical analysis of the data
- Identify risk factors for cardiac dyssynchrony
- Initial research is in the area of Heart Failure and Ventricular Dyssynchrony

## CVRG Challenges

- Using the Grid to allow researchers to discover, access, and correlate patient genomic, proteomic, electrophysiological, and imaging data
- Unique challenge is integrating three separate grids: 1) caBIG, 2) CVRG, and 3) BIRN
- Unify authentication and authorization between BIRN and CVRG
- Eventually want users from caBIG, BIRN, and CVRG to seamlessly access a “super-grid”

## AECG Workflow 

- RDT -> AECG XML

- HL7 AECG data service

- QTVi Output Data Service

- JPEG data service

- QTVi Analytical Service

- Store INI parameters

- Retrieve HL7 aECG

- Matlab viewholt

- Store
- ECG

- HL7
- aECG

- All data between services is XML

- ini data service

- JSDL service

- Invoke
- Processing

- JPEG
- output

- Record Job
- Execution

- Retrieve INI parameters

- QTVi Result

## How are the services developed?

- AECG, INI, QTVi, JSDL data services
- Created using XML Data Service Introduce Extension
- MatLab Analysis Service
- Communicate using MatLab java server/client
- Created using Introduce Analytical Service
- Implement “invoke” method with JSDL as input
- Implement parsing of JSDL
- Locate and retrieve input data from Data Service
- Translate data to and from MatLab algorithm compatible form
- Store output data to Data Service

## Applications

- CVRG aECG analysis workflow
**gridIMAGE Radiology Central Review**
- Microscopy Analysis and caMicroscope

## GridIMAGE

- A grid-based application that enables distributed access and review of DICOM images
- Supports cross-institute, geographically distributed research collaborations and clinical decision support
- Multiple CAD and research algorithms
- Multiple human reviewers
- Multiple data repositories with large number of subjects
- Distributed compute and storage systems
- Enable parameter studies and clinical and preclinical trials
- Facilitate algorithm development and validation through the use of many distributed, shared image datasets

## gridIMAGE - Central Review

- In Vivo Imaging Middleware used to create DICOM Data Service, Human Markup and CAD Analytical Service (caGrid 1.0 Gold Compliant Service)
- CAD Service upon invocation retrieves images from remote DICOM Data Service and submits CAD markups to a central result server
- Reviewers login to markup client and retrieve pending review requests
- Markup client retrieves images from DICOM Data Service and submits markup results to a central result server

**gridIMAGE - Central Review**

## Applications

- CVRG aECG analysis workflow
- gridIMAGE Radiology Central Review
**Microscopy Analysis and caMicroscope**

## caMicroscope: Neuroblastoma Classification

- Neuroblastoma is a childhood cancer
- International Neuroblastoma Prognosis Classification System developed by Shimada et al., classifies the disease into various prognostic groups in terms of different pathologic features
- In clinical practice, there are three typical criteria for classification of the neuroblastic tumors
- Our focus is on
- Grade of neuroblastic differentiation (undifferentiated, poorly-differentiated, and differentiating)
- The presence of Schwannian stromal development (stroma-poor and stroma-rich)

**Notes:**
NB is a childhood cancer. There is a classification system developed by Shimada et al. called the International NB Prognosis Classification System. This system tries to classify the disease into different groups based on different pathologic features. In practice, there are three criteria for classification. So far, my colleagues at OSU worked on the two criteria *READ*. You are going to see some presentations about this tomorrow. So, I am not going into details of this.

## Challenges in Neuroblastoma Classification

- The size of a single neuroblastoma image is in the order of a few Gigabytes when compressed
- A typical image repository contains data whose size is in the order of Terabytes
- Complicated, time-consuming image classification algorithms are required
- Sequential systems are not practical due to the massive size of the image data and hence the processing requirements, justifying the need for parallel large-scale data processing

**Notes:**
Unfortunately, automated classification has its own problems. First of all, the size of NB images are a few GB even when compressed. If you consider a whole image repository, its size could be in the order of Terabytes. Moreover, for processing these images, complicated algorithm are required and they are time-consuming. Consequently, sequential systems are not practical and we need appropriate HPC infrastructures with large-scale image processing capabilities. Our solution, as I said at the beginning is based on grid computing and parallel computing.

## gridIMAGE for Pathology (Neuroblastoma)

**gridIMAGE for Pathology (Neuroblastoma)**

- Interfaces with caGrid 1.0 Data and Analytical Services to execute pathology workflows.
- Interact with caGrid Data Services to retrieve image and store analyzed result images
- Transmit and Invoke algorithm with required inputs
- Algorithms are run on a MATLAB based analytical services that processes on a high performance compute cluster

## General System Architecture

**Notes:**
This is the grid service part. This forms a front-end for the backend parallel computing system, which has a master-client architecture. It contains a master application which distributed the images to the clients and the partial images are processed at the clients. The service also maintains a common data and code repository. So multiple clients can concurrently access and manage the same, shared repository.

## Parallel Backend

## Pathology Image Analysis Client

## caMicroscope

- caTissue

- Pathology Analysis

## caArray/geWorkbench

- caTissue

- caArray

- geWorkbench