Reliable Cell Tracking by Global Data Association

Abstract

Automated cell tracking in populations is important for research and discovery in biology and medicine. In this paper, we propose a cell tracking method based on global spatio-temporal data association which considers hypotheses of initialization, termination, translation, division and false positive in an integrated formulation. Firstly, reliable tracklets (i.e., short trajectories) are generated by linking detection responses based on frame-by-frame association. Next, these tracklets are globally associated over time to obtain final cell trajectories and lineage trees. During global association, tracklets form tree structures where a mother cell divides into two daughter cells. We formulate the global association for tree structures as a maximum-a-posteriori (MAP) problem and solve it by linear programming. This approach is quantitatively evaluated on sequences with thousands of cells captured over several days.

Meeting Name

International Symposium on Biomedical Imaging (2011: Mar. 30-Apr. 2, Chicago, IL)

Department(s)

Computer Science

Keywords and Phrases

Automated Cell; Biology and Medicine; Cell Tracking; Daughter Cells; False Positive; Global Data; Maximum a Posteriori; Spatio-Temporal Data; Tree Structures; Medical Imaging; Plant Extracts; Population Statistics; Trees (Mathematics); Cells; Global Data Association

International Standard Book Number (ISBN)

978-1424441280

International Standard Serial Number (ISSN)

1945-7928

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2011 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Mar 2011

Link to Full Text

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