Detail publikačního výsledku

Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model

VIČAR, T.

Originální název

Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model

Anglický název

Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Fluorescence nuclei images, nuclei tracking, Gaussian mixture model

Klíčová slova v angličtině

Fluorescence nuclei images, nuclei tracking, Gaussian mixture model

Autoři

VIČAR, T.

Rok RIV

2019

Vydáno

26.04.2018

Nakladatel

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních

Místo

Brno

ISBN

978-80-214-5614-3

Kniha

Proceedings of the 24th Conference STUDENT EEICT 2018

Strany od

590

Strany do

593

Strany počet

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"
}

Dokumenty

eeict_vicar_final_po_oprave