Analytical Cellular Pathology

Analytical Cellular Pathology / 2010 / Article

Open Access

Volume 33 |Article ID 918306 |

Yinhai Wang, David McCleary, Ching-Wei Wang, Paul Kelly, Jackie James, Dean A. Fennell, Peter Hamilton, "Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing", Analytical Cellular Pathology, vol. 33, Article ID 918306, 15 pages, 2010.

Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing


Background: Tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.Methods: A High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.Results: The automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.Conclusion: The methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.

Copyright © 2010 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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