Transcript Efficient Processing of Pathological Images Using the Grid: Computer-Aided Prognosis of Neuroblastoma

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, poorlydifferentiated, 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
Background
Cytoplasm
Neuropil
Nuclei
Segmented
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