Detailed Information on Publication Record
2015
Next Step Toward the Automation of Screening for Cervical Cancer
SVOBODA, DavidBasic information
Original name
Next Step Toward the Automation of Screening for Cervical Cancer
Authors
Edition
Cytometry Part A, John Wiley & Sons, 2015, 1552-4922
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 3.181
Organization unit
Faculty of Informatics
UT WoS
000349984200003
Keywords in English
bright-field optical microscopy; simulations; Pap-smear specimen; benchmark datasets
Tags
Tags
International impact
Změněno: 29/4/2016 06:13, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
The simulations have always been of a great importance as they substitute the real processes when those are too expensive to be performed or impossible to be observed. The latter case is typical for optical microscopy. Here, we observe fixed or living cells under assumption, that the optical system and the attached electronic acquisition device do not affect the quality of the original specimen too much. Even though we can measure the most of optical aberrations and estimate the dominant sources of noise that together cause the final observed image to be blurred and noisy, we are still not able to reveal the original unaffected image how it would appear without any damage. Although several deconvolution methods are capable of inverting this degradation process, they can improve the quality of the data only to some extent. As there exists no exact knowledge, how the microscopic specimens look like, it is very difficult to evaluate the quality of new emerging segmentation and tracking algorithms that are of a great importance in medicine and biology. The same issue arises when one wants to tune-up their parameters. In the past, the only available quality measurement of the algorithms was an expert’s knowledge. The expert either classified the results of selected algorithm 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 synthetic data, naturally accompanied by their ground truth, have started to appear. In the very beginning, only the basic geometric shapes like spheres or disc without any texture representing the internal structure of the observed cells were used. Since the late 90s, computer generated images have started to be more complex as the computer capabilities rose and allowed for calculations that required higher performance and extensive memory and disk usage. Namely, in the last 10 years, several simulation frameworks able to gener- ate for example cells with detailed description of subcellular components, large cell populations, and time-lapse image sequences emerged.
Links
| GBP302/12/G157, research and development project |
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