.png){kind=icon}
返回
Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.media.2010.07.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A growing number of screening applications require the automated monitoring of cell populations in a high-throughput, high-content environment. These applications depend on accurate cell tracking of individual cells that display various behaviors including mitosis, merging, rapid movement, and entering and leaving the field of view. Many approaches to cell tracking have been developed in the past, but most are quite complex, require extensive post-processing, and are parameter intensive. To overcome such issues, we present a general, consistent, and extensible tracking approach that explicitly models cell behaviors in a graph-theoretic framework. We introduce a way of extending the standard minimum-cost flow algorithm to account for mitosis and merging events through a coupling operation on particular edges. We then show how the resulting graph can be efficiently solved using algorithms such as linear programming to choose the edges of the graph that observe the constraints while leading to the lowest overall cost. This tracking algorithm relies on accurate denoising and segmentation steps for which we use a wavelet-based approach that is able to accurately segment cells even in images with very low contrast-to-noise. In addition, the framework is able to measure and correct for microscope defocusing and stage shift. We applied the algorithms on nearly 6000 images of 400,000 cells representing 32,000 tracks taken from five separate datasets, each composed of multiple wells. Our algorithm was able to segment and track cells and detect different cell behaviors with an accuracy of over 99%. This overall framework enables accurate quantitative analysis of cell events and provides a valuable tool for high-throughput biological studies. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Cell analysis
Tracking
Segmentation
Minimum-cost flow
Wavelets
期刊
IF:
11.8
论文数:
3.9K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
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
OAAI
OAAI分享收藏Feature point tracking and trajectory analysis for video imaging in cell biology用于细胞生物学视频成像的特征点跟踪和轨迹分析
PREAI
分享收藏
查看更多内容