This project is to develop, deploy, and disseminate a suite of open source tools and integrated informatics platform that will facilitate multi-scale, correlative analyses of high resolution whole slide tissue image data, spatially mapped genetics and molecular data for cancer research. This platform will play an essential role in supporting studies of tumor initiation, development, heterogeneity, invasion, and metastasis. These tools will allow quantitative analyses of the interplay between morphology and spatially mapped genetics and molecular data and will be used in studies that predict outcome and response to treatment, in radiogenomic and quantitative radiology imaging studies and in studies to identify cancer targets. The software and methods will enable researchers to assemble and visualize detailed, multi-scale descriptions of tissue morphologic changes originating from a wide range of microscopy instruments and make it possible to efficiently manage, interrogate, and explore microscopy imaging data at multiple scales and to identify and analyze features across individuals and cohorts. The project will build on and extend the software and methods we have developed in microscopy imaging, integrative image analysis, high performance computing, databases, and visualization over the past fifteen years and will also leverage, integrate and adapt the Harvard Slicer platform. The design and implementation of the informatics platform will be driven by four well funded, leading edge cancer focused studies along with many additional collaborative efforts including the Cancer Imaging Archive (TCIA), the Mayo Clinic Quantitative Imaging Network site, the Colon Cancer Family Registry and the Polyp Prevention Study.

Public Health Relevance

Cancer is a disease that involves complex interactions between cancer cells, surrounding and distant tissue. Within a given cancer, cancer cells can differ from one another in many ways and cancer cells can exert a variety of types of influence on other tissue. In order to develop effective diagnostic and treatment methods for cancer, we need to understand these complex patterns of interaction. This project will develop and deploy a suite of informatics tools that will enable researchers to study tumors - their structure, their genetics and protein expression - at microscopic scales. Our tools will be employed by basic cancer researchers who study cancer mechanisms, by researchers who seek to discover new therapies and by researchers who employ quantitative imaging methods to assess results of clinical cancer trials.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Ossandon, Miguel
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
State University New York Stony Brook
Schools of Medicine
Stony Brook
United States
Zip Code
Hou, Le; Samaras, Dimitris; Kurc, Tahsin M et al. (2016) Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016:2424-2433
Louis, David N; Feldman, Michael; Carter, Alexis B et al. (2016) Computational Pathology: A Path Ahead. Arch Pathol Lab Med 140:41-50
Gao, Yi; Ratner, Vadim; Zhu, Liangjia et al. (2016) Hierarchical nucleus segmentation in digital pathology images. Proc SPIE Int Soc Opt Eng 9791:
Gao, Yi; Liu, William; Arjun, Shipra et al. (2016) Multi-scale learning based segmentation of glands in digital colonrectal pathology images. Proc SPIE Int Soc Opt Eng 9791:
Kurc, Tahsin; Qi, Xin; Wang, Daihou et al. (2015) Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies. BMC Bioinformatics 16:399
Almeida, Jonas S; Hajagos, Janos; Crnosija, Ivan et al. (2015) OpenHealth Platform for Interactive Contextualization of Population Health Open Data. AMIA Annu Symp Proc 2015:297-305
Gomes, Jeremias M; Teodoro, George; de Melo, Alba et al. (2015) Efficient irregular wavefront propagation algorithms on Intel(®) Xeon Phi(™). Proc Symp Comput Archit High Perform Comput 2015:25-32
Andrade, G; Ferreira, R; Teodoro, George et al. (2014) Efficient Execution of Microscopy Image Analysis on CPU, GPU, and MIC Equipped Cluster Systems. Proc Symp Comput Archit High Perform Comput 2014:89-96
Teodoro, George; Pan, Tony; Kurc, Tahsin et al. (2014) Region Templates: Data Representation and Management for High-Throughput Image Analysis. Parallel Comput 40:589-610