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Artificial neural networks for high performance fully automated particle tracking analysis even at low signal to noise regimes

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41GM123897-01
Agency Tracking Number: R41GM123897
Amount: $224,997.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 400
Solicitation Number: PA16-303
Timeline
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-05-01
Award End Date (Contract End Date): 2019-04-30
Small Business Information
106 OAK SPRING CT
Carrboro, NC 27510-2504
United States
DUNS: 080335601
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 SAMUEL LAI
 (919) 966-3024
 lai@unc.edu
Business Contact
 YING-YING WANG
Phone: (248) 982-7470
Email: yingyingw@gmail.com
Research Institution
 UNIV OF NORTH CAROLINA CHAPEL HILL
 
104 AIRPORT DRIVE, CB#1350
CHAPEL HILL, NC 27599-0001
United States

 Nonprofit college or university
Abstract

Abstract Particle tracking PT is a powerful biophysical tool for elucidating molecular interactions transport
phenomena and rheological properties in complex biological environments Unfortunately PT remains a niche
tool in life and physical sciences with a limited user base in large part due to significant time and technical
constraints in extracting accurate time variant positional data from recorded movies These constraints are
exacerbated in experiments with low signal to noise ratios or substantial heterogeneity as frequently
encountered with nanoparticles and pathogens in biological fluids Currently available software that attempts
to automate the movie analysis process rely almost exclusively on assigning static image filters based on
specific intensity pixel size and signal to noise ratio thresholds Unfortunately when applied to actual
experimental data with substantial spatial and temporal heterogeneity the current software generally produces
substantial numbers of false positives i e tracking artifacts or false negatives i e missing actual traces and
frequently both Frequent user intervention is thus required to ensure accurate tracking even when using
sophisticated tracking software markedly reducing experimental throughput and resulting in substantial user
to user variations in analyzed data The time required for accurate particle tracking analysis makes PT
experiments exceedingly expensive compared to other commonly used experimental techniques in life
sciences These same tracking analysis limitations have effectively precluded investigators from undertaking
more sophisticated D PT even though the microscopy capability to obtain such movies is readily available
and critical scientific insights can be gained from D PT To circumvent the challenges with currently available
particle tracking software we have developed a new approach for particle identification and tracking based on
machine learning and convolutional neural networks CNN CNN is a type of feed forward artificial neural
network designed to process information in a layered network of connections that mimics the organization of
real neural networks in the mammalian retina and visual cortex Unlike most CNN imaging models that are
trained to make predictions on static images we have trained our CNN to input adjacent frames so that each
prediction includes information from the past and future thus effectively performing convolutions in both space
and time to infer particle locations Similar principles of image analysis are now being harnessed by
developers of autonomous vehicle technologies to distinguish the motions of different objects on the road We
have applied our CNN tracking algorithm to a wide range of D movies capturing dynamic motions of
nanoparticles viruses and highly motile bacteria achieving at least fold time savings with virtually no need
for human intervention while maintaining robust tracking performance i e low false positive and low false
negative rates In this STTR proposal we seek to focus on further optimization and testing of our neural
network tracking platform for D PT including the use of cloud computing Aim and extending our neural
network tracker to enable accurate D PT Aim Our vision is to popularize PT as a research tool among
researchers by minimizing the time and labor costs associated with PT analysis Narrative
Particle tracking is a powerful biophysical tool in life and physical sciences but unfortunately its application has
been strongly limited by inefficiencies in accurately extracting particle traces from raw movies Unlike
conventional particle tracking methods we have combined artificial intelligence and machine learning to create
a computational neural network that can recognize objects in much the same way as the human eye and
which consistently provided superior and truly automated tracking performance compared to current
alternatives This STTR will establish the feasibility of using our computational neural network for robust D
and D particle tracking analysis

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

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