High Performance Computing for Tissue MicroArray Analysis Dr Yinhai Wang David McCleary, Ching-Wei Wang, Jackie James, Dean Fennell, Peter Hamilton

Introduction

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, 7 th Edition, Chapter 7, 2004.

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

103,790×58,586 pixels 17GB 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?

Objective

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

Materials and Methods

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

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

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

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

HPC: Analytic Module Texture feature calculation ◦ Tumour pattern recognition ◦ Tumour region identification

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

HPC: Digital Slide Serving Module

Results

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

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

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

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.

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.

Acknowledgements

Thanks Dr Yinhai Wang 0044-(0)