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Web-Based Infrastructure for Comparison and Validation of Image Computing Methods

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 9R42MH106302-02
Agency Tracking Number: R42MH106302
Amount: $977,223.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: 101
Solicitation Number: PA13-089
Timeline
Solicitation Year: 2014
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-08-22
Award End Date (Contract End Date): 2016-07-31
Small Business Information
28 CORPORATE DRIVE
Clifton Park, NY 12065-8688
United States
DUNS: 010926207
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 STEPHEN AYLWARD
 (919) 966-9695
 aylward@unc.edu
Business Contact
 WILLIAM SCHROEDER
Phone: (518) 331-1177
Email: will.schroeder@kitware.com
Research Institution
 UNIVERSITY OF UTAH
 
75 SOUTH 2000 EAST
SALT LAKE CITY, UT 84112-8930
United States

 Nonprofit college or university
Abstract

DESCRIPTION provided by applicant We propose to develop the infrastructure for and deploy a commercial installation of an Algorithm Evaluation Service AES The service will help bridge the gap between algorithm researchers and commercial product developers It will assist commercial company in determining which medical image analysis and informatics algorithms they should integrate into their products and provide algorithm researchers with better access to clinically relevant amounts of data and with a better understanding of clinical and commercial needs Specifically the AES will provide a service whereby commercial organizations can post clinical data analysis challenges data metrics and awards tied to performance milestones related to their intended products and researchers can easily incorporate those challenges into their algorithm development workflows Our successful Phase I grant culminated with our prototype system maturing and serving as the online infrastructure for the Multimodal Brain Tumor Segmentation BRATS Grand Challenge at MICCAI and the Prostate Segmentation Grand Challenge at ISBI Herein we propose to Aim extend our system to support a novel mechanism for algorithm submission based on virtual machine technology that addresses clinical integration i e multi step data processing including human interaction security and computational resource scalability to support extensive testing We will Aim extend existing software development tools i e our popular CMake build system to make the submission of algorithms to AES challenges an inherent and effortless part of algorithm development for researchers We will Aim validate the resulting system using additional grand challenges and we will deliver it to and receive feedback from our first commercial customer as part of the proposed work Specifically two academic groups Ohio State University and The University of Utah have agreed to conduct grand challenges using our systems Additionally a commercial group Imaging Endpoints has agreed to serve as our first commercial customer They are an imaging core lab that provides algorithmic solutions to pharmaceutical companies and clinical research organizations They will use our AES to post a clientandapos s data and metrics offer a prize and thereby attract algorithm developers to
generate solutions to their clientandapos s problem It is generally accepted that a chasm exists between algorithm researchers the capabilities of medical devices and the needs of clinical practice The proposed work will help bridge that chasm and will operate as a viable business model PUBLIC HEALTH RELEVANCE A chasm exists between researchers and medical device manufacturers Commercial companies have a difficult time determining which medical data analysis algorithms they should integrate into their products because research publications often involve only limited clinical evaluations Similarly algorithm researchers are often frustrated by how little access they have to clinically relevant data and commercial needs We propose to deploy an Algorithm Evaluation Service AES whereby commercial organizations can post clinical data analysis challenges data metrics and awards tied to performance milestones related to their intended products and researchers can easily incorporate those challenges into their algorithm development workflows The needs of commercial companies and researchers will be met and more effective commercial solutions to healthcare problems will result

* Information listed above is at the time of submission. *

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