Efficient Processing of Pathological Images Using the Grid: Computer-Aided Prognosis of Neuroblastoma B. Barla Cambazoglu Ohio 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