Paper Info
Title
Automated Cell Segmentation With 3d Fluorescence Microscopy Images
Abstract
A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional ( 3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow ( GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with ( 1) small cell false detection and missing rates for individual cells; and ( 2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.
Year
Venue
Keywords
2015
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
Fluorescence Microscopy Image, 3D Cell Analysis, Gradient Vector Flow
Field
DocType
Volume
Voxel,Computer vision,Image gradient,Fluorescence-lifetime imaging microscopy,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Image segmentation,Vector flow,Artificial intelligence,Thresholding
Conference
2015
ISSN
Citations 
PageRank 
1945-7928
1
0.36
References 
Authors
3
7
Name
Order
Citations
PageRank
Jun Kong110617.74
Fusheng Wang2173.14
George Teodoro315022.18
Yanhui Liang4166.50
Yangyang Zhu550.79
Carol Tucker-Burden610.36
Daniel J. Brat7859.91