Efficient Processing of Pathological Images Using the Grid:

Computer-Aided Prognosis of Neuroblastoma

B. Barla CambazogluOhio State University

Department of Biomedical Informatics

Overview

• Neuroblastoma classification problem

• Grid overview

• Grid-enabled parallel computing solution

• Experimental results

• On-going work

Neuroblastoma Classification Problem

• Neuroblastoma is a childhood cancer

• Peripheral neuroblastic tumors are a group of embryonal tumors of the sympathetic nervous system

• 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, two typical criteria for classification of the neuroblastic tumors are– Grade of neuroblastic differentiation (undifferentiated, poorly-

differentiated, and differentiating)

– The presence of Schwannian stromal development (stroma-poor and stroma-rich)

Sample Neuroblastoma Images

• In the current clinical practice, prognosis of neuroblastoma is largely dependent on the examination of haematoxylin- and eosin-stained tissue images by expert pathologists under the microscope– considerably time consuming– subject to inter- and intra-reader variations

Sample Segmentation

Original image

SegmentedNeuropil

Nuclei

Cytoplasm

Background

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

Grid for Biomedical Applications

• The collaborative nature of the grids– Lets scientists share distributed resources and applications– Eliminates the need for replication and waste of resources– Fosters the collaboration among developers

• Large computational resources offered by the grid– Large memory and storage capacities– Distributed computational resources

• The grid comes with built-in security mechanisms– Authentication– Authorization– Encryption

Grid-Enabled Neuroblastoma Classification

• Service-based infrastructure– Multiple, geographically distributed scientists and

developers access a common image data repository– Share a common code repository allowing reusability of

the developed codes– Remote job execution

• A multi-processor backend– Fast parallel processing of images– Specifically designed for very large-scale image processing– Pipelined processing capabilities

General System Architecture

Neuroblastoma Grid Service

• The service is developed– Based on the caGrid 1.0 middleware– Using Introduce service development toolkit

• Strongly-typed interfaces

• Provided operations on images/algorithms– Query

• CQL (caGrid Query Language)

– Retrieve/Upload• Bulk data transfer• GridFTP

– Execute

Grid Service Client

Parallel Backend

Execution Times

Speedups (Single Reader)

Speedups (Multi-Reader)

On-going/Future Work

• Integration of the demand-driven code with the multi-reader code

• Dynamic service deployment

• Security infrastructure– Adaptation from In Vivo Imaging Middleware