arrow
返回

Spatio-temporal cell cycle phase analysis using level sets and fast marching methods

delete2009-02-01
delete114
PRE
AI
D
Dirk Padfield *
J
Jens Rittscher
N
Nick Thomas
B
Badrinath Roysam
DOI:10.1016/j.media.2008.06.018delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Enabled by novel molecular markers, fluorescence microscopy enables the monitoring of multiple cellular functions using live cell assays. Automated image analysis is necessary to monitor such model systems in a high-throughput and high-content environment. Here, we demonstrate the ability to simultaneously track cell cycle phase and cell motion at the single cell level. Using a recently introduced cell cycle marker, we present a set of image analysis tools for automated cell phase analysis of live cells over extended time periods. Our model-based approach enables the characterization of the four phases of the cell cycle G1, S, G2, and M, which enables the study of the effect of inhibitor compounds that are designed to block the replication of cancerous cells in any of the phases. We approach the tracking problem as a spatio-temporal volume segmentation task, where the 2D slices are stacked into a volume with time as the z dimension. The segmentation of the G2 and S phases is accomplished using level sets, and we designed a model-based shape/size constraint to control the evolution of the level set. Our main contribution is the design of a speed function coupled with a fast marching path planning approach for tracking cells across the G1 phase based on the appearance change of the nuclei. The viability of our approach is demonstrated by presenting quantitative results on both controls and cases in which cells are treated with a cell cycle inhibitor. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
Cell cycle phase
Segmentation
Tracking
Level sets
Fast marching
Path planning
Model-based analysis
Shape and size constraint
Automated image analysis
Cell cycle phase marker
High-throughput
High-content
Confocal fluorescence imaging
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Medical Image Analysis 封面图
Medical Image Analysis
IF:
11.8
论文数:
3.8K
被引数:
2.4W

机构

G
General Electric
学者数:
4.4K
论文数: 3.4K
被引数: 2
R
rensselaer polytechnic institute
学者数:
7.0K
论文数: 6.5K
被引数: 6
引用论文

引用论文

Diurnal patterns of airborne pollen concentration of the selected tree and herb taxa in Gdańsk (northern Poland)
err2005-09-01
err0
errOAAI
errMałgorzata Latałowa; Agnieszka Uruska; Anna Pe˛dziszewska; Małgorzata Góra; Anna Dawidowska
err分享
err收藏
Development and Validation of the University of Washington Clinical Assessment of Music Perception Test华盛顿大学音乐知觉临床评估测试的开发和验证
err2009-08-01
err0
errOAAI
errRobert Kang; Grace Liu Nimmons; Ward Drennan; Jeff Longnion; Chad Ruffin; Kaibao Nie; Jong Ho Won; Tina Worman; Bevan Yueh; Jay Rubinstein
err分享
err收藏
Variational level set approach to multiphase motion
err1996-08-01
err952
PREAI
errZhao, HK; Chan, T; Merriman, B; Osher, S
err分享
err收藏
Risk of lung cancer from radon exposure: contribution of recently published studies of uranium miners
err2012-10-01
err0
errOAAI
errM. Tirmarche; J. Harrison; D. Laurier; E. Blanchardon; F. Paquet; J. Marsh
err分享
err收藏
学者 查看更多内容