On Generative Modeling of Cell Shape Using 3D GANs
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107522" target="_blank" >RIV/00216224:14330/19:00107522 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-30645-8_61" target="_blank" >http://dx.doi.org/10.1007/978-3-030-30645-8_61</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-30645-8_61" target="_blank" >10.1007/978-3-030-30645-8_61</a>
Alternative languages
Result language
angličtina
Original language name
On Generative Modeling of Cell Shape Using 3D GANs
Original language description
The ongoing advancement of deep-learning generative models, showing great interest of the scientific community since the introduction of the generative adversarial networks (GAN), paved the way for generation of realistic data. The utilization of deep learning for the generation of realistic biomedical images allows one to alleviate the constraints of the parametric models, limited by the employed mathematical approximations. Building further upon the laid foundation, the 3D GAN added another dimension, allowing generation of fully 3D volumetric data. In this paper, we present an approach to generating fully 3D volumetric cell masks using GANs. Presented model is able to generate high-quality cell masks with variability matching the real data. Required modifications of the proposed model are presented along with the training dataset, based on 385 real cells captured using the fluorescence microscope. Furthermore, the statistical validation is also presented, allowing to quantitatively assess the quality of data generated by the proposed model.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-05048S" target="_blank" >GA17-05048S: Multi-modal live cell image segmentation and tracking</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Image Analysis and Processing – ICIAP 2019
ISBN
9783030306441
ISSN
0302-9743
e-ISSN
—
Number of pages
11
Pages from-to
672-682
Publisher name
Springer
Place of publication
Trento
Event location
Trento, Italy
Event date
Jan 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000562008400059