b. barla cambazog lu ohio state university department of biomedical informatics
DESCRIPTION
Efficient Processing of Pathological Images Using the Grid: Computer-Aided Prognosis of Neuroblastoma. B. Barla Cambazog lu Ohio State University Department of Biomedical Informatics. Overview. Neuroblastoma classification problem Grid overview Grid-enabled parallel computing solution - PowerPoint PPT PresentationTRANSCRIPT
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