Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing. (2010)

• Record Type: Journal Article
• Authors: Wang, Yinhai; McCleary, David; Wang, Ching-Wei; Kelly, Paul; James, Jackie; Fennell, Dean A.; Hamilton, Peter
• Is Part Of: Analytical cellular pathology. Volume 33:Number 5/6(2010)
• Page Start: 271
• Page End: 285
• Publication Date: 2010
• Subjects: Cluster -- dynamic load balancing -- high performance computing -- parallel processing -- Tissue MicroArray -- TMA -- virtual slide; Pathology, Cellular -- Periodicals; Cytology -- Periodicals; Oncology -- Periodicals; Cancer -- Cytopathology -- Periodicals; Cancer -- Cytopathology; Cytology; Oncology; Pathology, Cellular; Cell Transformation, Neoplastic; Cells -- pathology; Cytological Techniques; Genetic Techniques; Periodicals; 571.936
• DOI: 10.3233/ACP-CLO-2010-0551
• Languages: English
• ISSNs: 2210-7177

Abstract

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. 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.