Analysis of high-throughput microscopy videos: Catching up with cell dynamics

A. Arbelle; N. Drayman; M. Bray; U. Alon; A. Carpenter; T. Riklin Raviv

doi:10.1007/978-3-319-24574-4_26

Analysis of high-throughput microscopy videos: Catching up with cell dynamics

A. Arbelle, N. Drayman, M. Bray, U. Alon, A. Carpenter, T. Riklin Raviv

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
Editors	Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
Publisher	Springer Verlag
Pages	218-225
Number of pages	8
ISBN (Print)	9783319245737
DOIs	https://doi.org/10.1007/978-3-319-24574-4_26
State	Published - 2015
Externally published	Yes
Event	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: 5 Oct 2015 → 9 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9351
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Country/Territory	Germany
City	Munich
Period	5/10/15 → 9/10/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-24574-4_26

Cite this

Arbelle, A., Drayman, N., Bray, M., Alon, U., Carpenter, A., & Raviv, T. R. (2015). Analysis of high-throughput microscopy videos: Catching up with cell dynamics. In A. F. Frangi, N. Navab, J. Hornegger, & W. M. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings (pp. 218-225). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351). Springer Verlag. https://doi.org/10.1007/978-3-319-24574-4_26

Arbelle, A. ; Drayman, N. ; Bray, M. et al. / Analysis of high-throughput microscopy videos : Catching up with cell dynamics. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. editor / Alejandro F. Frangi ; Nassir Navab ; Joachim Hornegger ; William M. Wells. Springer Verlag, 2015. pp. 218-225 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Analysis of high-throughput microscopy videos: Catching up with cell dynamics",

abstract = "We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.",

author = "A. Arbelle and N. Drayman and M. Bray and U. Alon and A. Carpenter and Raviv, {T. Riklin}",

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year = "2015",

doi = "10.1007/978-3-319-24574-4_26",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Arbelle, A, Drayman, N, Bray, M, Alon, U, Carpenter, A & Raviv, TR 2015, Analysis of high-throughput microscopy videos: Catching up with cell dynamics. in AF Frangi, N Navab, J Hornegger & WM Wells (eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, Springer Verlag, pp. 218-225, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, Germany, 5/10/15. https://doi.org/10.1007/978-3-319-24574-4_26

Analysis of high-throughput microscopy videos: Catching up with cell dynamics. / Arbelle, A.; Drayman, N.; Bray, M. et al.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. ed. / Alejandro F. Frangi; Nassir Navab; Joachim Hornegger; William M. Wells. Springer Verlag, 2015. p. 218-225 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Analysis of high-throughput microscopy videos

T2 - 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015

AU - Arbelle, A.

AU - Drayman, N.

AU - Bray, M.

AU - Alon, U.

AU - Carpenter, A.

AU - Raviv, T. Riklin

PY - 2015

Y1 - 2015

N2 - We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.

AB - We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.

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DO - 10.1007/978-3-319-24574-4_26

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 218

EP - 225

BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings

A2 - Frangi, Alejandro F.

A2 - Navab, Nassir

A2 - Hornegger, Joachim

A2 - Wells, William M.

PB - Springer Verlag

Y2 - 5 October 2015 through 9 October 2015

ER -

Arbelle A, Drayman N, Bray M, Alon U, Carpenter A, Raviv TR. Analysis of high-throughput microscopy videos: Catching up with cell dynamics. In Frangi AF, Navab N, Hornegger J, Wells WM, editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. Springer Verlag. 2015. p. 218-225. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-24574-4_26

Analysis of high-throughput microscopy videos: Catching up with cell dynamics

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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