B. Barla Cambazog lu Ohio State University Department of Biomedical Informatics

1. Efficient Processing of Pathological Images Using the Grid:Computer-Aided Prognosis of Neuroblastoma B. Barla Cambazoglu Ohio State University Department of Biomedical Informatics

2. Overview • Neuroblastoma classification problem • Grid overview • Grid-enabled parallel computing solution • Experimental results • On-going work

3. 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)

4. 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

5. Sample Segmentation Background Original image Nuclei Cytoplasm Neuropil Segmented

6. 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

7. 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

8. 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

9. General System Architecture

10. 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

11. Grid Service Client

12. Parallel Backend

13. Execution Times

14. Speedups (Single Reader)

15. Speedups (Multi-Reader)

16. 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