A unified framework for automated 3-d segmentation of surface-stained living cells and a comprehensive segmentation evaluation.
This work presents a unified framework for whole cell segmentation of surface stained living cells from 3-D data sets of fluorescent images. Every step of the process is described, image acquisition, prefiltering, ridge enhancement, cell segmentation, and a segmentation evaluation. The segmentation results from two different automated approaches for segmentation are compared to manual segmentation of the same data using a rigorous evaluation scheme. This revealed that combination of the respective cell types with the most suitable microscopy method resulted in high success rates up to 97%. The described approach permits to automatically perform a statistical analysis of various parameters from living cells.
Hodneland
Erlend
E
Department of Biomedicine, University of Bergen, 5009 Bergen, Norway. erlend.hodneland@biomed.uib.no
Bukoreshtliev
Nickolay V
NV
Eichler
Tilo W
TW
Tai
Xue-Cheng
XC
Gurke
Steffen
S
Lundervold
Arvid
A
Gerdes
Hans-Hermann
HH
D000465:Algorithms; D000818:Animals; D002462:Cell Membrane / Q000648:ultrastructure*; D002478:Cells, Cultured; D003627:Data Interpretation, Statistical; D005456:Fluorescent Dyes; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence; D016011:Normal Distribution; D016716:PC12 Cells; D051381:Rats; D015203:Reproducibility of Results; D013382:Subtraction Technique
D005456:Fluorescent Dyes