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Ultra-fast processing of gigapixel Tissue MicroArray images using High Performance Computing

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An Erratum to this article was published on 01 October 2011

Abstract

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.

Conclusions

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.

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Notes

  1. Processing time in Fig. 6 may appear to be negative as the result of how it is calculated. In our test, the processing time for texture feature calculation is obtained as the time difference between two separate runs. The first iteration calculated the overall processing time for Image Loading, texture feature calculation and Image Saving, whereas the second iteration only calculated the overall processing time for Image Loading and Image Saving.

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Acknowledgement

This work was supported by the Department of Employment and Learning through its “ Strengthening the all-Island Research Base” initiative.

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Authors and Affiliations

  1. Centre for Biomedical Informatics, Queen’s University Belfast, Health Science Building, 97 Lisburn Road, Belfast, BT9 7BL, UK

    Yinhai Wang, Ching-Wei Wang, Dean A. Fennell & Peter W. Hamilton

  2. Centre for Cancer Research and Cell Biology (CCRCB), Queen’s University Belfast, Belfast, UK

    Yinhai Wang, Ching-Wei Wang, Jackie James & Dean A. Fennell

  3. i-Path Diagnostics Ltd, Belfast, UK

    David McCleary

  4. Department of Pathology, Royal Group of Hospitals, Belfast, UK

    Paul Kelly

Authors
  1. Yinhai Wang
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  2. David McCleary
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  3. Ching-Wei Wang
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  4. Paul Kelly
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  5. Jackie James
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  6. Dean A. Fennell
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  7. Peter W. Hamilton
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Corresponding author

Correspondence to Peter W. Hamilton.

Additional information

This paper is a reprint from ‘Ultra-fast Processing of Gigapixel Tissue MicroArray Images using High Performance Computing, Yinhai Wang, David McCleary, Ching-Wei Wang, Paul Kelly, Jackie James, Dean A. Fennell and Peter Hamilton” originally published in Analytical Cellular Pathology/Cellular Oncology, Volume 33, number 5–6, 2010, pp. 271–285, IOS Press.

An erratum to this article can be found at http://dx.doi.org/10.1007/s13402-011-0063-3

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Wang, Y., McCleary, D., Wang, CW. et al. Ultra-fast processing of gigapixel Tissue MicroArray images using High Performance Computing. Cell Oncol. 34, 495–507 (2011). https://doi.org/10.1007/s13402-011-0046-4

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  • DOI: https://doi.org/10.1007/s13402-011-0046-4

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