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Intelligent and Automatic Image Segmentation Software for High ThroughputAnalysi

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41AR064596-01
Agency Tracking Number: R41AR064596
Amount: $292,838.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NIAMS
Solicitation Number: PAR09-221
Timeline
Solicitation Year: 2013
Award Year: 2013
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
105 HAYNES CIR
NICHOLASVILLE, KY 40356-8852
United States
DUNS: 78357046
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 KARYN ESSER
 (859) 327-1845
 cytoinformaticsllc@gmail.com
Business Contact
 KARYN ESSER
Phone: (859) 327-1845
Email: cytoinformaticsllc@gmail.com
Research Institution
 UNIVERSITY OF KENTUCKY RESEARCH FOUNDATION
 
UNIVERSITY OF KENTUCKY 109 KINKEAD HALL
LEXINGTON, KY 40506-0057
United States

 () -
 Nonprofit College or University
Abstract

DESCRIPTION (provided by applicant): It is well established that aging and many chronic diseases, such as cancer and heart failure, are associated with significant losses in skeletal muscle mass and strength in humans. There is agreement across the musclebiology community that important morphological characteristics of muscle fibers, such as fiber area, the number and position of myonuclei, cellular infiltration and fibrosis are critical factors that determine the health and function of the muscle. However, at this time, quantification of muscle characteristics from standard histological and immunohistological techniques is still a manual or, at best, a semi-automatic process. This process is labor intensive and can be prone to errors, leading to high inter-observer variability. On the other hand, when muscle characteristics are calculated by computer-aided image analysis, data acquisition times decrease and objectivity improves significantly. The objective of this Phase I STTR project is to build a fully automatic, intelligent, and high throughput image acquisition and analysis software for quantitative muscle morphological analysis on digitized muscle cross-sections. We propose to utilize the most recent technical advances in machine learning and biomedicalimage analysis. This includes a newly developed deformable model and mean-shift based seed detection algorithm for better segmentation accuracy; an asymmetric online boosting based machine learning algorithm which allows the software to learn from errorsand adjust its segmentation strategies adaptively; and a data parallelization schema using the graphic processing unit (GPU) to handle the computational bottleneck for extremely large scale image, such as whole slide scanned specimens. We believe that thissoftware, equipped with the most advanced technical innovations, will be commercially attractive for the skeletal muscle research community including basic scientists, clinician scientists, and the pharmaceutical industry. The specific aim are: 1) Develop, implement, and validate an automatic biological image analysis software package for skeletal muscle tissue; 2) Develop a novel online updated intelligent artificial intelligence unit to enable the software to learn from errors; 3) Build a novel high performance computing unit to enable fast and high throughput automatic image analysis, which is capable of processing whole slide scanned muscle specimens. The analysis approach proposed will provide more consistent, accurate, and objective quantification ofskeletal muscle morphological properties and the time for data analysis will be reduced by over a factor of 100 for standalone version and 2000 for parallel version. The long-term goal of Cytoinformatics, LLC for the Phase II stage is to apply the softwareto analyze histology/pathology from human muscle biopsy samples and extension of the software to other biological tissues, such as adipose tissue. PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: Important features of muscle fibers, such as fiber area, the number and position of myonuclei, cellular infiltration and fibrosis are critical factors that determine the health of the muscle. However quantification of muscle features from digitized images is still a manual or, at best, a semi-automaticprocess. The objective of this Phase I STTR project is to build software using the most recent technical advances in machine learning and biomedical image analyses to significantly move the skeletal muscle basic and clinical research fields ahead.

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

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