A quantitative three-dimensional image analysis tool for maximal acquisition of spatial heterogeneity data
File(s)
Accepted version
Author(s)
Allenby, MC
Misener, R
Panoskaltsis, N
Mantalaris, A
Type
Journal Article
Abstract
Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.
Date Issued
2017-01-09
Date Acceptance
2017-01-09
Citation
Tissue Engineering Part C-Methods, 2017, 23 (2), pp.108-117
ISSN
1937-3392
Publisher
Mary Ann Liebert
Start Page
108
End Page
117
Journal / Book Title
Tissue Engineering Part C-Methods
Volume
23
Issue
2
Copyright Statement
© 2017 Mary Ann Liebert, Inc. Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/ten.TEC.2016.0413
Sponsor
Royal Academy Of Engineering
Commission of the European Communities
Imperial College Trust
Identifier
http://www.ncbi.nlm.nih.gov/pubmed/28068883
Grant Number
10216/118
340719
N/A
Subjects
3D cultures
image analysis
spatial heterogeneity
Biomedical Engineering
0601 Biochemistry And Cell Biology
0903 Biomedical Engineering
Publication Status
Published
Coverage Spatial
United States