Image Mining for Analysis of Expression Patterns in Tissue Microarray

Software produced by this project!

The capacity to distinguish among subclasses of disease affects how patients are treated, which medications are appropriate, and what levels of risk are justified. Tissue microarray (TMA) technology makes it possible to investigate and confirm clinico-pathologic correlations which have been postulated based upon the evaluation of whole histology sections. Unfortunately, inconsistencies often arise during the evaluation process as a result of subjective impressions and inter- and intra-observer variability. Advanced imaging and computational tools make it possible to detect and track subtle changes in measurable parameters leading to insight regarding the underlying mechanisms of disease progression and the discovery of novel diagnostic and prognostic clues which are not apparent by human inspection alone.

The overarching goals of this renewal application are to build upon progress made in the first phase of the project and design, develop and evaluate new capabilities by meeting the objectives of the following specific aims:

(1) Develop and evaluate a new family of multi-stage, searching algorithms to facilitate quick, reliable interrogation of larg-scale, clinical and research, microscopy applications including whole-slide imaging and tissue microarray;

(2) Develop and evaluate a suite of high-throughput services capable of automatically detecting, archiving and indexing user-specified objects (e.g. tissues, cells) in large collections of images and implement extensions to the data models and support for optimized pipeline selection. These capabilities will enable large-scale correlative outcomes studies and support expansion of the "gold standard" image archives and correlated clinical repositories. The services will take advantage of state-of-the-art parallel CPU-GPU machines and the searching algorithms described in Aim 1;

(3) Optimize the imaging, computational and content-based image retrieval algorithms and tools using a wide range of different tissues, cancer types and biomarkers to support clinical and research experiments and studies involving patient stratification, quality-control, and outcomes assessment; and

(4) Deploy the analytical tools, data models, user-centered interfaces and reference libraries of imaged specimens to participating adopter sites to conduct open-set usability and performance studies and make these resources available to the clinical and research communities as open source software and resources to support future development and testing of new hypotheses, algorithms and methods.


09/2013 to 08/2018

Image Mining for Comparative Analysis of Expression Patterns in Tissue Microarrays. Role: Principal Investigator w/ Joel Saltz. (2R01LM009239-05A1). NIH. Funded.

09/2017 to 08/2018
Busch Biomedical Grant Program

02/2018 to 01/2020
Methods and Tools for Integrating Pathomics Data into Cancer Registries. RoleL Principal Investigator w/ Joel Saltz. (1UG3 CA225021-01) . NIH. Funded

Collaborating Groups

Biomedical Image Computing and Imaging Informatics (Dr. Lin Yang)

Lin Yang is an associate professor with the J. Crayton Pruitt Family Department of Biomedical Engineering at University of Florida. He leads the Biomedical Image Computing and Imaging Informatics (BICI2) Lab. He was recruited to University of Florida in 2014 as one of the first round preeminence hirings (36 faculty members) through UF Preeminence Hiring Plan.

Department of Biomedical Informatics at Stony Brook

Joel H. Saltz, MD, PhD, Cherith Professor and Founding Chair, Department of Biomedical Informatics; Vice President for Clinical Informatics, Stony Brook Medicine; Associate Director, Stony Brook University Cancer Center.