文献类型： 会议论文

第一作者： Young Hwan Chang

作者： Young Hwan Chang ¹ ; Jeremy Linsley ² ; Josh Lamstein ² ; Jaslin Kalra ² ; Irina Epstein ² ; Mariya Barch ² ; Kenneth Daily ³ ; Phil Synder ³ ; Larsson Omberg ³ ; Laura Heiser ¹ ; Steve Finkbeiner ² ;

作者机构： 1.Oregon Health & Science University, Portland, OR

2.Gladstone Center for Systems and Therapeutics, San Francisco, CA

3.Sage Bionetworks, Seattle, WA, USA

会议名称： IEEE Conference on Decision and Control

主办单位：

页码： 5086-5091

摘要： Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as "stable matching problem" and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.

分类号： TP13