2014
The Importance of Computer Generated Data in Fluorescence Microscopy
SVOBODA, DavidZákladní údaje
Originální název
The Importance of Computer Generated Data in Fluorescence Microscopy
Autoři
SVOBODA, David (203 Česká republika, garant, domácí)
Vydání
Advances in Molecular and Cancer Biology, 2014
Další údaje
Jazyk
angličtina
Typ výsledku
Vyžádané přednášky
Obor
20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Stát vydavatele
Česká republika
Utajení
není předmětem státního či obchodního tajemství
Kód RIV
RIV/00216224:14330/14:00073963
Organizační jednotka
Fakulta informatiky
Klíčová slova anglicky
simulation; biomedical imaging; fluorescence microscopy; cell nucleus
Příznaky
Mezinárodní význam
Změněno: 15. 10. 2014 16:37, doc. RNDr. David Svoboda, Ph.D.
Anotace
V originále
In fluorescence microscopy, the proper evaluation of image segmentation and tracking algorithms, that are further used for observation of some particular processes or directly to diagnosis, is still an open task. The problem is based on the fact that currently there exists no exact knowledge, how the microscopic specimens look like if observed without any degradation introduced by the microscope setup. In the past, the only available quality measurement of the algorithms was an expert's knowledge. The expert either classified the results of selected algorithms or provided an annotation of some real image dataset that was further used for evaluation purposes. Both ways however suffer from two main issues. First, the expert's evaluation is nondeterministic. Second, for higher dimensional data (sequences of 2D or 3D images) the handmade annotation is impractical or even impossible. For this reason, the computer generated data, naturally accompanied by their ground truth, have started to appear. In this talk, a survey of the most important toolkits employed for generation of synthetic image data containing cells in fluorescence microscopy is given. We also mention the results achieved by the group CBIA. We will present a toolbox that can generate fully 3D digital phantoms of specific cellular components along with their corresponding images degraded by specific optics and electronics. The images can represent static scenes (fixed cells) as well as time-lapse sequences (living cells). Such synthetically generated images can serve as a benchmark dataset for measuring the quality of various segmentation and tracking algorithms.
Návaznosti
GBP302/12/G157, projekt VaV |
|