Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development

Marcelo Cicconet; Michelle Gutwein; Kristin C. Gunsalus; Davi Geiger

doi:10.1016/j.compbiomed.2014.04.011

Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development

Marcelo Cicconet, Michelle Gutwein, Kristin C. Gunsalus, Davi Geiger

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell sta≥ and (5) statistical analysis of various timing distributions obtained from those examples.

Original language	English (US)
Pages (from-to)	24-34
Number of pages	11
Journal	Computers in Biology and Medicine
Volume	51
DOIs	https://doi.org/10.1016/j.compbiomed.2014.04.011
State	Published - Aug 1 2014

Keywords

Cell counting
Database
Dynamic programming
Embryo development
Event detection
Time series
Tracking

ASJC Scopus subject areas

Computer Science Applications
Health Informatics

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10.1016/j.compbiomed.2014.04.011

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title = "Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development",

abstract = "In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell sta≥ and (5) statistical analysis of various timing distributions obtained from those examples.",

keywords = "Cell counting, Database, Dynamic programming, Embryo development, Event detection, Time series, Tracking",

author = "Marcelo Cicconet and Michelle Gutwein and Gunsalus, {Kristin C.} and Davi Geiger",

note = "Funding Information: The authors would like to thank Yael Kramer, Caroline McCaffrey, and Nicole Noyes of the NYU Fertility Center for assistance with time-lapse image data acquisition. This work was supported in part by NIH grants R01-GM085503 (to KCG and NN) and R01-HD046236 (to Fabio Piano and KCG). ",

year = "2014",

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doi = "10.1016/j.compbiomed.2014.04.011",

language = "English (US)",

volume = "51",

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AU - Cicconet, Marcelo

AU - Gutwein, Michelle

AU - Gunsalus, Kristin C.

AU - Geiger, Davi

N1 - Funding Information: The authors would like to thank Yael Kramer, Caroline McCaffrey, and Nicole Noyes of the NYU Fertility Center for assistance with time-lapse image data acquisition. This work was supported in part by NIH grants R01-GM085503 (to KCG and NN) and R01-HD046236 (to Fabio Piano and KCG).

PY - 2014/8/1

Y1 - 2014/8/1

N2 - In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell sta≥ and (5) statistical analysis of various timing distributions obtained from those examples.

AB - In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell sta≥ and (5) statistical analysis of various timing distributions obtained from those examples.

KW - Cell counting

KW - Database

KW - Dynamic programming

KW - Embryo development

KW - Event detection

KW - Time series

KW - Tracking

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ER -

Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development

Abstract

Keywords

ASJC Scopus subject areas

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