LAP-based tracking for focal adhesions

Katerina Lomanov; Jesus Martinez del Rincon; Paul Miller; Hugh Gribben

LAP-based tracking for focal adhesions

Katerina Lomanov, Jesus Martinez del Rincon, Paul Miller, Hugh Gribben

School of Electronics, Electrical Engineering and Computer Science

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

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Abstract

We present a focal adhesion (FA) detector and tracker based on an Interacting Multiple Model (IMM) algorithm combined with the double use of the linear assignment problem (LAP) for data association. Performance has been evaluated using a real confocal microscopy image sequence of a migrating cell stained for FAs. The tracker has been compared against several state-of-the-art, commercially available tracking algorithms. With a multiple object tracking accuracy (MOTA) of 31.6, it outperforms each of these other methods.

Original language	English
Title of host publication	Irish Machine Vision and Image Processing Conference Proceedings 2018: Proceedings
Pages	49-56
Publication status	Published - 2018
Event	20th Irish Machine Vision and Image Processing Conference - Ulster University, Belfast, United Kingdom Duration: 29 Aug 2018 → 31 Aug 2018

Conference

Conference	20th Irish Machine Vision and Image Processing Conference
Abbreviated title	IMVIP
Country/Territory	United Kingdom
City	Belfast
Period	29/08/2018 → 31/08/2018

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LAP-based tracking for focal adhesions
© 2018 The Authors and The Irish Pattern Recognition & Classification Society. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 813 KBLicence: Unspecified

https://iprcs.scss.tcd.ie/pdf/IMVIP2018Book.pdf#page=59Licence: Unspecified

Detection and tracking of cells and focal adhesions in microscopy images
Lomanov, K. (Author), Miller, P. (Supervisor) & Martinez del Rincon, J. (Supervisor), Dec 2020
Student thesis: Doctoral Thesis › Doctor of Philosophy

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@inproceedings{dd002339c6414e399ad8dc1c9cca7421,

title = "LAP-based tracking for focal adhesions",

abstract = "We present a focal adhesion (FA) detector and tracker based on an Interacting Multiple Model (IMM) algorithm combined with the double use of the linear assignment problem (LAP) for data association. Performance has been evaluated using a real confocal microscopy image sequence of a migrating cell stained for FAs. The tracker has been compared against several state-of-the-art, commercially available tracking algorithms. With a multiple object tracking accuracy (MOTA) of 31.6, it outperforms each of these other methods.",

author = "Katerina Lomanov and {Martinez del Rincon}, Jesus and Paul Miller and Hugh Gribben",

year = "2018",

language = "English",

isbn = "978-0-9934207-3-3. ",

pages = "49--56",

booktitle = "Irish Machine Vision and Image Processing Conference Proceedings 2018: Proceedings",

note = "20th Irish Machine Vision and Image Processing Conference, IMVIP ; Conference date: 29-08-2018 Through 31-08-2018",

}

TY - GEN

T1 - LAP-based tracking for focal adhesions

AU - Lomanov, Katerina

AU - Martinez del Rincon, Jesus

AU - Miller, Paul

AU - Gribben, Hugh

PY - 2018

Y1 - 2018

N2 - We present a focal adhesion (FA) detector and tracker based on an Interacting Multiple Model (IMM) algorithm combined with the double use of the linear assignment problem (LAP) for data association. Performance has been evaluated using a real confocal microscopy image sequence of a migrating cell stained for FAs. The tracker has been compared against several state-of-the-art, commercially available tracking algorithms. With a multiple object tracking accuracy (MOTA) of 31.6, it outperforms each of these other methods.

AB - We present a focal adhesion (FA) detector and tracker based on an Interacting Multiple Model (IMM) algorithm combined with the double use of the linear assignment problem (LAP) for data association. Performance has been evaluated using a real confocal microscopy image sequence of a migrating cell stained for FAs. The tracker has been compared against several state-of-the-art, commercially available tracking algorithms. With a multiple object tracking accuracy (MOTA) of 31.6, it outperforms each of these other methods.

M3 - Conference contribution

SN - 978-0-9934207-3-3.

SP - 49

EP - 56

BT - Irish Machine Vision and Image Processing Conference Proceedings 2018: Proceedings

T2 - 20th Irish Machine Vision and Image Processing Conference

Y2 - 29 August 2018 through 31 August 2018

ER -

LAP-based tracking for focal adhesions

Abstract

Conference

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Student theses

Detection and tracking of cells and focal adhesions in microscopy images

Cite this