Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

Tahsin Kurc*, Xin Qi, Daihou Wang, Fusheng Wang, George Teodoro, Lee Alex Donald Cooper, Michael Nalisnik, Lin Yang, Joel Saltz, David J. Foran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Background: We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands. Results: The proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system. Conclusions: Our work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.

Original languageEnglish (US)
Article number399
JournalBMC bioinformatics
Issue number1
StatePublished - Dec 1 2015


  • Databases
  • High performance computing
  • gPUs

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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