PMID- 22096600
OWN - NLM
STAT- MEDLINE
DCOM- 20120410
LR  - 20211021
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 6
IP  - 11
DP  - 2011
TI  - Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular 
      bright field microscopy images.
PG  - e27593
LID - 10.1371/journal.pone.0027593 [doi]
LID - e27593
AB  - Confocal microscopy analysis of fluorescence and morphology is becoming the 
      standard tool in cell biology and molecular imaging. Accurate quantification 
      algorithms are required to enhance the understanding of different biological 
      phenomena. We present a novel approach based on image-segmentation of 
      multi-cellular regions in bright field images demonstrating enhanced quantitative 
      analyses and better understanding of cell motility. We present MultiCellSeg, a 
      segmentation algorithm to separate between multi-cellular and background regions 
      for bright field images, which is based on classification of local patches within 
      an image: a cascade of Support Vector Machines (SVMs) is applied using basic 
      image features. Post processing includes additional classification and graph-cut 
      segmentation to reclassify erroneous regions and refine the segmentation. This 
      approach leads to a parameter-free and robust algorithm. Comparison to an 
      alternative algorithm on wound healing assay images demonstrates its superiority. 
      The proposed approach was used to evaluate common cell migration models such as 
      wound healing and scatter assay. It was applied to quantify the acceleration 
      effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a 
      time lapse confocal microscopy wound healing assay and demonstrated that the 
      healing rate is linear in both treated and untreated cells, and that HGF/SF 
      accelerates the healing rate by approximately two-fold. A novel fully automated, 
      accurate, zero-parameters method to classify and score scatter-assay images was 
      developed and demonstrated that multi-cellular texture is an excellent descriptor 
      to measure HGF/SF-induced cell scattering. We show that exploitation of textural 
      information from differential interference contrast (DIC) images on the 
      multi-cellular level can prove beneficial for the analyses of wound healing and 
      scatter assays. The proposed approach is generic and can be used alone or 
      alongside traditional fluorescence single-cell processing to perform objective, 
      accurate quantitative analyses for various biological applications.
FAU - Zaritsky, Assaf
AU  - Zaritsky A
AD  - Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
FAU - Natan, Sari
AU  - Natan S
FAU - Horev, Judith
AU  - Horev J
FAU - Hecht, Inbal
AU  - Hecht I
FAU - Wolf, Lior
AU  - Wolf L
FAU - Ben-Jacob, Eshel
AU  - Ben-Jacob E
FAU - Tsarfaty, Ilan
AU  - Tsarfaty I
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20111109
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - *Algorithms
MH  - Animals
MH  - Cell Line
MH  - Cell Line, Tumor
MH  - Cell Movement/*physiology
MH  - Dogs
MH  - Image Processing, Computer-Assisted
MH  - Mice
MH  - Microscopy/*methods
MH  - Microscopy, Confocal
MH  - Support Vector Machine
PMC - PMC3212570
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2011/11/19 06:00
MHDA- 2012/04/11 06:00
PMCR- 2011/11/09
CRDT- 2011/11/19 06:00
PHST- 2011/06/04 00:00 [received]
PHST- 2011/10/20 00:00 [accepted]
PHST- 2011/11/19 06:00 [entrez]
PHST- 2011/11/19 06:00 [pubmed]
PHST- 2012/04/11 06:00 [medline]
PHST- 2011/11/09 00:00 [pmc-release]
AID - PONE-D-11-09946 [pii]
AID - 10.1371/journal.pone.0027593 [doi]
PST - ppublish
SO  - PLoS One. 2011;6(11):e27593. doi: 10.1371/journal.pone.0027593. Epub 2011 Nov 9.