Publication result detail

Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model

VIČAR, T.

Original Title

Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model

English Title

Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model

Type

Paper in proceedings (conference paper)

Original Abstract

The life cell microscopic imaging is a standard approach for studying of cancer cell morphology and behaviour during some treatment. In the dense cell cultures, tracking each cell nucleus is challenging task due to cell overlap and interactions. Moreover, for time-lapse sequences (lasting typically 20-30 hours) the robust automatic cell tracking is needed. This paper describes new method for fluorescence nuclei tracking based on Gaussian mixture model (GMM), and additionally, GMM modification allowing application to the images is also introduced. Method is mainly designed for robustness - tracking the highest possible number of nuclei in the whole sequence. Proposed algorithm proved to by very reliable with 80% of correctly tracked nuclei.

English abstract

The life cell microscopic imaging is a standard approach for studying of cancer cell morphology and behaviour during some treatment. In the dense cell cultures, tracking each cell nucleus is challenging task due to cell overlap and interactions. Moreover, for time-lapse sequences (lasting typically 20-30 hours) the robust automatic cell tracking is needed. This paper describes new method for fluorescence nuclei tracking based on Gaussian mixture model (GMM), and additionally, GMM modification allowing application to the images is also introduced. Method is mainly designed for robustness - tracking the highest possible number of nuclei in the whole sequence. Proposed algorithm proved to by very reliable with 80% of correctly tracked nuclei.

Keywords

Fluorescence nuclei images, nuclei tracking, Gaussian mixture model

Key words in English

Fluorescence nuclei images, nuclei tracking, Gaussian mixture model

Authors

VIČAR, T.

RIV year

2019

Released

26.04.2018

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních

Location

Brno

ISBN

978-80-214-5614-3

Book

Proceedings of the 24th Conference STUDENT EEICT 2018

Pages from

590

Pages to

593

Pages count

4

URL

http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf

BibTex

@inproceedings{BUT147410,
  author="Tomáš {Vičar}",
  title="Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model",
  booktitle="Proceedings of the 24th Conference STUDENT EEICT 2018",
  year="2018",
  number="první",
  pages="590--593",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních",
  address="Brno",
  isbn="978-80-214-5614-3",
  url="http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf"
}

Documents

eeict_vicar_final_po_oprave