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

• Type of Material: Article
• Authors: Tahsin Kurc, Stony Brook University; Xin Qi, Rutgers University; Daihou Wang, Rutgers University; Fusheng Wang, Stony Brook University; George Teodoro, Stony Brook University; Lee Cooper, Emory University; Michael Nalisnik, Emory University; Lin Yang, University of Florida; Joel Saltz, Stony Brook University; David J. Foran, Rutgers University
• Language: English
• Date: 2015-12-01
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2015 Kurc et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12859-015-0831-6
• Title of Journal or Parent Work: BMC Bioinformatics
• ISSN: 1471-2105
• Volume: 16
• Start Page: 399
• End Page: 399
• Grant/Funding Information: This research used resources provided by the XSEDE Science Gateways program under grant TG-ASC130023, the Keeneland Computing Facility at the Georgia Institute of Technology, supported by the NSF under Contract OCI-0910735, and the Nautilus system at the University of Tennessee’s Center for Remote Data Analysis and Visualization supported by NSF Award ARRA-NSF-OCI-0906324.; This work was funded in part by HHSN261200800001E from the NCI, 1U24CA180924-01A1 from the NCI, 5R01LM011119-05 and 5R01LM009239-07 from the NLM, and CNPq.
• Keywords: High performance computing; DATABASES; GPUs; Life Sciences & Biomedicine; MEDICAL APPLICATIONS; CONTENT-BASED RETRIEVAL; Biotechnology & Applied Microbiology; SYSTEM; ALGORITHMS; VISUALIZATION; CLASSIFICATION; Biochemistry & Molecular Biology; Mathematical & Computational Biology; CLUSTER ENSEMBLES; Science & Technology; IDENTIFICATION; RADIOLOGY; Databases; Biochemical Research Methods
• Research Categories: Biology, Bioinformatics; Computer Science; Engineering, Biomedical

Abstract

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.