title: Ultra-fast processing of gigapixel Tissue MicroArray images using high performance computing.
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.
authors.affiliation: Centre for Biomedical Informatics, Queen's University Belfast, Health Science Building, 97 Lisburn Road, Belfast, BT9 7BL, UK.
authors.forename: Yinhai
authors.initials: Y
authors.lastname: Wang
authors.forename: David
authors.initials: D
authors.lastname: McCleary
authors.forename: Ching-Wei
authors.initials: CW
authors.lastname: Wang
authors.forename: Paul
authors.initials: P
authors.lastname: Kelly
authors.forename: Jackie
authors.initials: J
authors.lastname: James
authors.forename: Dean A
authors.initials: DA
authors.lastname: Fennell
authors.forename: Peter W
authors.initials: PW
authors.lastname: Hamilton