Dr Yinhai Wang David McCleary, Ching-Wei Wang, Jackie James, Dean Fennell, Peter Hamilton

1. Dr Yinhai Wang David McCleary, Ching-Wei Wang, Jackie James, Dean Fennell, Peter Hamilton

2. Introduction

3. Tissue Microarrays Key technique for high throughput single assay platform for tissue biomarker research and discovery. *Dolled-Filhart and Rimm, Principles and Practice of Oncology, 7th Edition, Chapter 7, 2004.

4. The Bottleneck Relies on visual scoring of tissue biomarkers by pathologists. It is time consuming, subjective and prone to error. 232 Tissue Cores

5. Image Analysis of TMA Virtual Slides • A TMA virtual slide is an ultra-large digital image, scanned at a high magnification (40X). • Computer assisted analysis using TMA virtual slides. • Objective and reproducible. • Speed? 103,790×58,586 pixels 17GB

6. Objective

7. Objective • Automate TMA analysis • Genuine high throughput platform • Reduce pathologists workload • Speedup biomarker discovery

8. Materials and Methods

9. High Performance Computing (HPC) Platform • Hewlett-Packard Blade Server. • Intel Xeon quad-core x86_64 processors. • >9,000 processor-cores available. • 10-16GB memory per node (8 cores). • Gigabit Ethernet connection. • Fibre connection to hard disks (SAN).

10. High Performance Computing Image generation Visualisation HPC Platform Digital Slide Serving Module Glass slide TMA Database Viewing Instructions Analytical Module Image File Access Module Results Parallel Processing Module High resolution image

11. JPEG 2000 HPC: Image File Access Module uncompressed data JPEG Hamamatsu virtual slide Carl Zeiss virtual slide Aperio virtual slide Yes JPEG 2000 decoder JPEG decoder JPEG decoder JPEG decoder Raw image Raw image Raw image Pixel (n,0) Pixel (0,1) Region extraction No Colour conversion R B B R G G G G G G B R B G R G B R B R uncompressed data uncompressed data a Format? Compressed? Compressed? Compressed? Yes Pixel (0,0) Pixel (0,0) Pixel (0,1) Pixel (0,1) No Region extraction Colour conversion R Yes Vendor format independent No Region extraction Colour conversion B

12. HPC: Parallel Processing Module 6 Analyse and return results 6 Analyse and return results Informs Master it is now available 7 Database Master Worker 1 Worker 2 Worker 3 Worker 4 1 Request for core coordinates D1 C1 A1 B1 Storage Retrieve and Load Core Sub-image 3 2 Assign to available workers 5 TMA core location (x, y) at TMA virtual slide D2 C2 B2 A2 4 Locate Image in Storage and Core Sub-image Centralised Dynamic Load Balancing

13. HPC: Analytic Module Texture feature calculation • Tumour pattern recognition • Tumour region identification

14. HPC: Analytic Module Automated quantisation of biomarker IHC density on TMA core images, using colour decomposition.

15. HPC: Digital Slide Serving Module www.pathxl.com

16. Results

17. Texture Pattern Calculation for TMA Slides • 106,290×65,017 pixels • 19.3GB • 229 Tissue Cores Speedup=(Fastest Sequential Code)/(Parallel Code)=42.58

18. Loading, Storing, Texture Time for Loading vs. Saving Time for actual Texture Feature Calculation

19. Biomarker Quantification Processing time: 30minutes77seconds Speedup=22.19

20. Multi TMA Slides • There are >9000 processor-cores available • The processing of 1 TMA virtual slide uses <100 processor-cores. • >90 TMA virtual slides can be processed simultaneously (≈1 minute). • Genuine high throughput platform for multiplex multi-TMA studies.

21. Conclusion • A novel high performance computing platform for the rapid analysis of TMA virtual slides. • The centralised load balancing approach is proven to be robust. • It significantly speedups up the analysis of TMAs, removing the bottleneck. • Valuable platform for TMA research & biomarker discovery. • High performance platform for the algorithm prototyping, development & evaluation.

22. Acknowledgements

23. Thanks Dr Yinhai Wang y.wang@qub.ac.uk 0044-(0)-28-9097 5816